Methods and systems for transesophageal echocardiogram guided implantation of a stent and valve device

Abstract

A system for and method of transesophageal echocardiogram-guided implantation of a stent are disclosed. The system includes at least a transesophageal echocardiogram (TEE) system, at least a display and at least a computing device configured to receive a plurality of ultrasound images, generate at least a three-dimensional (3D) cardiac model as a function of the plurality of ultrasound images, receive at least a 3D stent model, determine a view label for each of the plurality of ultrasound images and display the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label. A system for and method of transesophageal echocardiogram-guided implantation of a valve device are also disclosed.

Description

[0001]METHODS AND SYSTEMS FOR TRANSESOPHAGEAL ECHOCARDIOGRAM GUIDED IMPLANTATION OF A STENT AND VALVE DEVICE CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority of Provisional Application Serial No. 63 / 705,376, filed on October 9, 2024, and entitled, “METHODS AND SYSTEMS FOR TRANSESOPHAGEAL ECHOCARDIOGRAM GUIDED IMPLANTATION OF LEFT ATRIAL APPENDAGE CLOSURE DEVICE” and U.S. Nonprovisional Application Serial No. 19 / 047,077, filed on February 6, 2025, and entitled “METHODS AND SYSTEMS FOR TRANSESOPHAGEAL ECHOCARDIOGRAM GUIDED IMPLANTATION OF A STENT,” and U.S. Nonprovisional Application Serial No.19 / 047,323, filed on February 6, 2025, and entitled “SYSTEM AND METHOD FOR TRANSESOPHAGEAL ECHOCARDIOGRAM- GUIDED IMPLANTATION OF A VALVE DEVICE,” both of which are incorporated by reference herein in their entirety. FIELD OF THE INVENTION The present invention generally relates to the field of implant placement. In particular, the present invention is directed to methods and systems for transesophageal echocardiogram guided implantation of a stent and systems and methods for transesophageal echocardiogram- guided implantation of a valve device. BACKGROUND Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, characterized by the narrowing or blockage of coronary arteries due to atherosclerotic plaque buildup. This condition restricts blood flow to the heart muscle, increasing the risk of angina, myocardial infarction (heart attack), and other severe cardiac complications. The buildup of plaque in the arterial walls causes stenosis, a condition that reduces the diameter of the artery and impairs its ability to deliver oxygenated blood to the myocardium. Stent placement is a widely adopted intervention to restore patency in stenosed coronary arteries. A stent is a small mesh tube designed to hold the artery open after it has been expanded, typically with the aid of a balloon catheter. Once deployed, the stent prevents arterial collapse and minimizes the risk of restenosis by maintaining blood flow to the affected region of the heart. However, placement of stents can be difficult and existing solutions do not provide adequate feedback to operators. 1 Attorney Docket No.1518-194PCT1 SUMMARY OF THE DISCLOSURE In an aspect, a system for transesophageal echocardiogram-guided implantation of a stent is disclosed. The system includes at least a transesophageal echocardiogram (TEE) system including at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect a plurality of ultrasound images as a function of cardiac tissue of the patient, at least a display and at least a computing device comprising at least a processor and a memory containing instructions configuring the at least a processor to receive the plurality of ultrasound images, generate at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images, receive at least a 3D stent model representative of a stent, determine a view label for each of the plurality of ultrasound images and display, using the at least a display, the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model includes superimposing the at least a 3D stent model onto the at least a 3D cardiac model. In an embodiment, determining the view label may include extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition. In an embodiment, determining the view label may include generating view training data, wherein the view training data comprises exemplary ultrasound images correlated to exemplary view labels, training a view classifier using the view training data, and determining the view label for each of the plurality of ultrasound images using the trained view classifier. In an embodiment, generating the 3D cardiac model may include generating the 3D cardiac model using a statistical shape model. In an embodiment, the 3D cardiac model may include peripheral vasculature. In an embodiment, receiving the at least a 3D stent model may include determining a stent datum as a function of at least a cardiac featuring datum and patient data and generating the at least a 3D stent model as a function of the stent datum. In an embodiment, generating the at least a 3D cardiac model may include generating the at least a 3D cardiac model using a point completion model. In an embodiment, displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model may include generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label and superimposing the at least a 3D stent model on to the pseudo TEE frame. In an embodiment, superimposing the at least a 3D stent model may include determining a position datum at the at least a 3D cardiac model as a function of a density 2 Attorney Docket No.1518-194PCT1 datum of at least a cardiac featuring datum and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum. In an embodiment, determining the position datum may include determining the position datum as a function of a user input received from a user interface presented on the at least a display. In an embodiment, displaying the at least a 3D cardiac model and the at least a 3D stent model may include generating a notification datum as a function of the position datum and the at least a 3D stent model. In an embodiment, superimposing the at least a 3D stent model onto the at least a 3D cardiac model may include determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model, generating a path model for the optimal path, and superimposing the path model onto the at least a 3D cardiac model. In another aspect, a method of transesophageal echocardiogram-guided implantation of a stent is disclosed. The method includes receiving, using at least a processor, a plurality of ultrasound images from at least a transesophageal echocardiogram (TEE) system including at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect the plurality of ultrasound images as a function of cardiac tissue of the patient. The method includes generating, using the at least a processor, at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images. In an embodiment, generating the 3D cardiac model may include generating the 3D cardiac model using a statistical shape model. In an embodiment, the 3D cardiac model may include peripheral vasculature. In an embodiment, generating the at least a 3D cardiac model may include generating the at least a 3D cardiac model using a point completion model. The method includes receiving, using the at least a processor, at least a 3D stent model representative of a stent. In an embodiment, receiving the at least a 3D stent model may include determining a stent datum as a function of at least a cardiac featuring datum and patient data and generating the at least a 3D stent model as a function of the stent datum. The method includes determining, using the at least a processor, a view label for each of the plurality of ultrasound images. In an embodiment, determining the view label may include extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition. In an embodiment, determining the view label may include generating view training data, wherein the view training data comprises exemplary ultrasound images correlated to exemplary view labels, training a view classifier using the view training data, and determining the view 3 Attorney Docket No.1518-194PCT1 label for each of the plurality of ultrasound images using the trained view classifier. The method includes displaying, using the at least a processor and at least a display, the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model includes superimposing the at least a 3D stent model onto the at least a 3D cardiac model. In an embodiment, displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model may include generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label and superimposing the at least a 3D stent model on to the pseudo TEE frame. In an embodiment, superimposing the at least a 3D stent model may include determining a position datum at the at least a 3D cardiac model as a function of a density datum of at least a cardiac featuring datum and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum. In an embodiment, determining the position datum may include determining the position datum as a function of a user input received from a user interface presented on the at least a display. In an embodiment, displaying the at least a 3D cardiac model and the at least a 3D stent model may include generating a notification datum as a function of the position datum and the at least a 3D stent model. In an embodiment, superimposing the at least a 3D stent model onto the at least a 3D cardiac model may include determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model, generating a path model for the optimal path, and superimposing the path model onto the at least a 3D cardiac model. In an aspect, a system for transesophageal echocardiogram-guided implantation of a stent, the system comprising at least a transesophageal echocardiogram (TEE) system comprising at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect a plurality of ultrasound images as a function of cardiac tissue of the patient, at least a display, and at least a computing device comprising at least a processor and a memory containing instructions configuring the at least a processor to receive the plurality of ultrasound images. The processor generates at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images by using a point completion model, wherein the point completion model uses a view- guided approach including a view-guided framework that retrieves absent global shape information of the heart from alternative single-view images for point cloud completion. In an 4 Attorney Docket No.1518-194PCT1 embodiment, generating the 3D cardiac model may include generating the 3D cardiac model using a statistical shape model. The processor receives at least a 3D stent model representative of a stent. In an embodiment, receiving the at least a 3D stent model may include determining a stent datum as a function of at least a cardiac featuring datum and patient data and generating the at least a 3D stent model as a function of the stent datum. The processor determines a view label for each of the plurality of ultrasound images. In an embodiment, determining the view label may include extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition. In an embodiment, determining the view label may include generating view training data, wherein the view training data comprises exemplary ultrasound images correlated to exemplary view labels, training a view classifier using the view training data, and determining the view label for each of the plurality of ultrasound images using the trained view classifier. The system displays, using the at least a display, at least a portion of the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises superimposing the at least a 3D stent model onto the at least a 3D cardiac model. In an embodiment, displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model may include generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label and superimposing the at least a 3D stent model on to the pseudo TEE frame. In an embodiment, superimposing the at least a 3D stent model may include determining a position datum at the at least a 3D cardiac model as a function of a density datum of at least a cardiac featuring datum and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum. In an embodiment, determining the position datum may include determining the position datum as a function of a user input received from a user interface presented on the at least a display. In an embodiment, displaying the at least a 3D cardiac model and the at least a 3D stent model may include generating a notification datum as a function of the position datum and the at least a 3D stent model. In an embodiment, superimposing the at least a 3D stent model onto the at least a 3D cardiac model may include determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model, generating a path model for the optimal path, and superimposing the path model onto the at least a 3D cardiac model. In an embodiment, the 3D cardiac model may include peripheral vasculature. 5 Attorney Docket No.1518-194PCT1 In another aspect, a method of transesophageal echocardiogram-guided implantation of a stent, the method comprising receiving, using at least a processor, a plurality of ultrasound images from at least a transesophageal echocardiogram (TEE) system comprising at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect the plurality of ultrasound images as a function of cardiac tissue of the patient. The method includes generating, using the at least a processor, at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images by using a point completion model, wherein the point completion model uses a view-guided approach including a view-guided framework that retrieves absent global shape information of the heart from alternative single-view images for point cloud completion. In an embodiment, generating the 3D cardiac model may include generating the 3D cardiac model using a statistical shape model. The method includes receiving, using the at least a processor, at least a 3D stent model representative of a stent. In an embodiment, receiving the at least a 3D stent model may include determining a stent datum as a function of at least a cardiac featuring datum and patient data and generating the at least a 3D stent model as a function of the stent datum. The method includes determining, using the at least a processor, a view label for each of the plurality of ultrasound images. In an embodiment, determining the view label may include extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition. In an embodiment, determining the view label may include generating view training data, wherein the view training data comprises exemplary ultrasound images correlated to exemplary view labels, training a view classifier using the view training data, and determining the view label for each of the plurality of ultrasound images using the trained view classifier. The method includes displaying, using the at least a processor and at least a display, the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises superimposing the at least a 3D stent model onto the at least a 3D cardiac model. In an embodiment, displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model may include generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label and superimposing the at least a 3D stent model on to the pseudo TEE frame. In an embodiment, superimposing the at least a 3D stent model may include determining a position datum at the at least a 3D cardiac model as a function of a density datum 6 Attorney Docket No.1518-194PCT1 of at least a cardiac featuring datum and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum. In an embodiment, determining the position datum may include determining the position datum as a function of a user input received from a user interface presented on the at least a display. In an embodiment, displaying the at least a 3D cardiac model and the at least a 3D stent model may include generating a notification datum as a function of the position datum and the at least a 3D stent model. In an embodiment, superimposing the at least a 3D stent model onto the at least a 3D cardiac model may include determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model, generating a path model for the optimal path, and superimposing the path model onto the at least a 3D cardiac model. In an embodiment, the 3D cardiac model may include peripheral vasculature. In an aspect, a system for transesophageal echocardiogram-guided implantation of a valve device is described. The system includes at least a transesophageal echocardiogram system including at least an ultrasound sensor configured to be located within an esophagus of a patient and detect at least an ultrasound image as a function of cardiac tissue of the patient. The system further includes a computing device configured to receive the at least an ultrasound image. The system generates at least a 3D cardiac model representative of a heart of the patient as a function of the at least an ultrasound image wherein the at least an ultrasound image includes a two- dimensional image of the heart of the patient. In an embodiment, generating the at least a 3D cardiac model may include extracting at least a cardiac feature from the at least an ultrasound image and segmenting the at least an ultrasound image into a plurality of image segments as a function of the at least a cardiac feature. In an embodiment, generating the at least a 3D cardiac model representative of the heart of the patient may include receiving a generic 3D model, identifying one or more anomalies within the at least an ultrasound image and generating the at least a 3D cardiac model as a function of the generic 3D model and the one or more anomalies using a statistical shape model. In an embodiment, at least one anomaly of the one or more anomalies may include a spatial distortion of at least one cardiac feature. In an embodiment, generating the at least a 3D cardiac model may include generating the at least a 3D cardiac model using a point completion model. The system determines a valve model datum as a function of the 3D cardiac model. The system receives at least valve model representative of at least a cardiovascular device to be placed within the patient as a function of the valve model 7 Attorney Docket No.1518-194PCT1 datum. In an embodiment, the valve model datum may include information associated with a dimension of the cardiovascular device. In an embodiment, receiving the at least valve model representative of the at least a cardiovascular device to be placed within the patient may include generating a search query for a device database as a function of the valve model datum, wherein the device database comprises a plurality of valve models representative of a plurality of cardiovascular devices and identifying at least one available valve model from the plurality of valve models as a function of the search query. In an embodiment, the search query may include an annulus diameter associated with the valve model. The system displays the at least a 3D cardiac model and the at least a valve model. In an embodiment, displaying the at least a 3D cardiac model and the at least a valve model may include generating a pseudo TEE frame as a function of the at least a 3D cardiac model and a view label, and superimposing the at least a valve model on to the pseudo TEE frame. In an embodiment, displaying the at least a 3D cardiac model and the at least valve model may include superimposing the at least a valve model onto to the 3D cardiac model to create a superimposed model and displaying the superimposed model. In an embodiment, displaying the superimposed model further may include displaying a path model for implantation of the cardiovascular device within the heart of the patient. In an embodiment, the 3D cardiac model may include peripheral vasculature. In another aspect, a method of transesophageal echocardiogram-guided implantation of a valve device is described. The method includes detecting, by at least a transesophageal echocardiogram system, at least an ultrasound image, wherein the at least a transesophageal echocardiogram system includes at least an ultrasound sensor configured to be located within an esophagus of a patient and detect the at least an ultrasound image as a function of cardiac tissue of the patient and receiving, by at least a computing device, the at least an ultrasound image. The method further includes generating, by the at least a computing device, at least a 3D cardiac model representative of a heart of the patient as a function of the at least an ultrasound image wherein the at least an ultrasound image includes a two-dimensional image of the heart of the patient. In an embodiment, generating the at least a 3D cardiac model may include extracting at least a cardiac feature from the at least an ultrasound image and segmenting the at least an ultrasound image into a plurality of image segments as a function of the at least a cardiac feature. In an embodiment, generating the at least a 3D cardiac model representative of the heart of the patient may include receiving a generic 3D model, identifying one or more anomalies within the 8 Attorney Docket No.1518-194PCT1 at least an ultrasound image and generating the at least a 3D cardiac model as a function of the generic 3D model and the one or more anomalies using a statistical shape model. In an embodiment, at least one anomaly of the one or more anomalies may include a spatial distortion of at least one cardiac feature. In an embodiment, generating the at least a 3D cardiac model may include generating the at least a 3D cardiac model using a point completion model. The method includes determining, by the at least a computing device, a valve model datum as a function of the 3D cardiac model. In an embodiment, the valve model datum may include information associated with a dimension of the cardiovascular device. The method includes receiving, by the at least a computing device, at least valve model representative of at least a cardiovascular device to be placed within the patient as a function of the valve model datum. In an embodiment, receiving the at least valve model representative of the at least a cardiovascular device to be placed within the patient may include generating a search query for a device database as a function of the valve model datum, wherein the device database comprises a plurality of valve models representative of a plurality of cardiovascular devices and identifying at least one available valve model from the plurality of valve models as a function of the search query. In an embodiment, the search query may include an annulus diameter associated with the valve model. The method includes displaying, by the at least a computing device, the at least a 3D cardiac model and the at least a valve model. In an embodiment, displaying the at least a 3D cardiac model and the at least a valve model may include generating a pseudo TEE frame as a function of the at least a 3D cardiac model and a view label, and superimposing the at least a valve model on to the pseudo TEE frame. In an embodiment, displaying the at least a 3D cardiac model and the at least valve model may include superimposing the at least a valve model onto to the 3D cardiac model to create a superimposed model and displaying the superimposed model. In an embodiment, displaying the superimposed model further may include displaying a path model for implantation of the cardiovascular device within the heart of the patient. In an embodiment, the 3D cardiac model may include peripheral vasculature. In an aspect, a system for transesophageal echocardiogram-guided implantation of a valve device, the system comprising at least a transesophageal echocardiogram system comprising at least an ultrasound sensor configured to be located within an esophagus of a patient and detect at least an ultrasound image as a function of cardiac tissue of the patient, and at least a computing device configured to receive the at least an ultrasound image. The system 9 Attorney Docket No.1518-194PCT1 generates at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the at least an ultrasound image wherein the at least an ultrasound image comprises a two-dimensional image of the heart of the patient. The system includes determining a valve model datum as a function of the 3D cardiac model. The system includes receiving at least a valve model representative of at least a cardiovascular device to be placed within the patient as a function of the valve model datum. In an embodiment, the valve model datum may include information associated with a dimension of the cardiovascular device. In an embodiment, generating the at least a 3D cardiac model may include extracting at least a cardiac feature from the at least an ultrasound image and segmenting the at least an ultrasound image into a plurality of image segments as a function of the at least a cardiac feature. In an embodiment, generating the at least a 3D cardiac model representative of the heart of the patient may include receiving a generic 3D model, identifying one or more anomalies within the at least an ultrasound image, and generating the at least a 3D cardiac model as a function of the generic 3D model and the one or more anomalies using a statistical shape model. In an embodiment, at least one anomaly of the one or more anomalies may include a spatial distortion of at least one cardiac feature. In an embodiment, receiving the at least valve model representative of the at least a cardiovascular device to be placed within the patient may include generating a search query for a device database as a function of the valve model datum, wherein the device database comprises a plurality of valve models representative of a plurality of cardiovascular devices, and identifying at least one available valve model from the plurality of valve models as a function of the search query. In an embodiment, the search query may include an annulus diameter associated with the valve model. In an embodiment, generating the at least a 3D cardiac model may include generating the at least a 3D cardiac model using a point completion model. The system includes generating at least a recommended cardiovascular device placement. The system includes displaying the at least a 3D cardiac model and the at least a valve model, wherein displaying the at least a 3D cardiac model and the at least a valve model comprises displaying an actual cardiovascular device placement overlaid on the recommended cardiovascular device placement. In an embodiment, displaying the at least a 3D cardiac model and the at least a valve model may include generating a pseudo transesophageal echocardiogram (TEE) frame as a function of the at least a 3D cardiac model and a view label and superimposing the at least a valve model on to the pseudo TEE frame. In an embodiment, displaying the at least a 3D cardiac model and the at least 10 Attorney Docket No.1518-194PCT1 valve model may include superimposing the at least a valve model onto to the 3D cardiac model to create a superimposed model and displaying the superimposed model. In an embodiment, displaying the superimposed model further may include displaying a path model for implantation of the cardiovascular device within the heart of the patient. In another aspect, a method for transesophageal echocardiogram-guided implantation of a valve device, the method comprising, detecting, by at least a transesophageal echocardiogram system, at least an ultrasound image, wherein the at least a transesophageal echocardiogram system comprises at least an ultrasound sensor configured to be located within an esophagus of a patient and detect the at least an ultrasound image as a function of cardiac tissue of the patient, receiving, by at least a computing device, the at least an ultrasound image. The method includes generating, by the at least a computing device, at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the at least an ultrasound image wherein the at least an ultrasound image comprises a two-dimensional image of the heart of the patient. In an embodiment, the valve model datum may include information associated with a dimension of the cardiovascular device. In an embodiment, generating the at least a 3D cardiac model may include extracting at least a cardiac feature from the at least an ultrasound image and segmenting the at least an ultrasound image into a plurality of image segments as a function of the at least a cardiac feature. In an embodiment, generating the at least a 3D cardiac model representative of the heart of the patient may include receiving a generic 3D model, identifying one or more anomalies within the at least an ultrasound image, and generating the at least a 3D cardiac model as a function of the generic 3D model and the one or more anomalies using a statistical shape model. In an embodiment, at least one anomaly of the one or more anomalies may include a spatial distortion of at least one cardiac feature. In an embodiment, receiving the at least valve model representative of the at least a cardiovascular device to be placed within the patient may include generating a search query for a device database as a function of the valve model datum, wherein the device database comprises a plurality of valve models representative of a plurality of cardiovascular devices, and identifying at least one available valve model from the plurality of valve models as a function of the search query. In an embodiment, the search query may include an annulus diameter associated with the valve model. In an embodiment, generating the at least a 3D cardiac model may include generating the at least a 3D cardiac model using a point completion model. The method includes determining, by the at least a computing device, a valve 11 Attorney Docket No.1518-194PCT1 model datum as a function of the 3D cardiac model, receiving, by the at least a computing device, at least a valve model representative of at least a cardiovascular device to be placed within the patient as a function of the valve model datum, generating at least a recommended cardiovascular device placement, and displaying, by the at least a computing device, the at least a 3D cardiac model and the at least a valve model, wherein displaying the at least a 3D cardiac model and the at least a valve model comprises displaying an actual cardiovascular device placement overlaid on the recommended cardiovascular device placement. In an embodiment, displaying the at least a 3D cardiac model and the at least a valve model may include generating a pseudo transesophageal echocardiogram (TEE) frame as a function of the at least a 3D cardiac model and a view label and superimposing the at least a valve model on to the pseudo TEE frame. In an embodiment, displaying the at least a 3D cardiac model and the at least valve model may include superimposing the at least a valve model onto to the 3D cardiac model to create a superimposed model and displaying the superimposed model. In an embodiment, displaying the superimposed model further may include displaying a path model for implantation of the cardiovascular device within the heart of the patient. DESCRIPTION OF DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG.1 illustrates a block diagram of an exemplary system for transesophageal echocardiogram- guided implantation of a stent; FIG.2 illustrates an exemplary display used during planning of implantation of a stent, according to some embodiments; FIG.3 illustrates an exemplary display used during implantation of a stent, according to some embodiments; FIG.4 illustrates an exemplary embodiment of a three-dimensional (3D) voxel occupancy representation; FIG.5 illustrates a schematic diagram of an exemplary transesophageal echocardiogram; FIG.6 illustrates a block diagram of an exemplary embodiment of a machine learning model; FIG.7 illustrates a schematic diagram of an exemplary embodiment of a neural network; FIG.8 illustrates a schematic diagram of an exemplary embodiment of a neural network node; 12 Attorney Docket No.1518-194PCT1 FIG.9 illustrates a flow diagram showing an exemplary planning method; FIG.10 illustrates a flow diagram showing an exemplary implantation method; FIG.11 illustrates a flow diagram showing an exemplary post-implantation method; FIG.12 illustrates a block diagram of an exemplary system for transesophageal echocardiogram- guided implantation of a valve device; FIG.13 illustrates an exemplary display used during planning of implantation of a cardiovascular device, according to some embodiments; FIG.14 illustrates an exemplary display used during implantation of cardiovascular device, according to some embodiments; FIG.15 illustrates a flow diagram showing an exemplary planning method; FIG.16 illustrates a flow diagram showing an exemplary implantation method; FIG.17 illustrates a flow diagram showing an exemplary post-implantation method; FIG.18 illustrates a flow diagram of an exemplary method for transesophageal echocardiogram- guided implantation of a valve device; FIG.19 illustrates a flow diagram of an exemplary method for transesophageal echocardiogram- guided implantation of a stent; and FIG.20 illustrates a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION At a high level, a system and method for transesophageal echocardiogram-guided implantation of a stent are disclosed. The system includes at least a transesophageal echocardiogram (TEE) system including at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect a plurality of ultrasound images as a function of cardiac tissue of the patient, at least a display and at least a computing device including at least a processor and a memory containing instructions configuring the at least a processor to receive the plurality of ultrasound images, generate at least 13 Attorney Docket No.1518-194PCT1 a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images, receive at least a 3D stent model representative of a stent, determine a view label for each of the plurality of ultrasound images and display, using the at least a display, the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model includes superimposing the at least a 3D stent model onto the at least a 3D cardiac model. In another aspect, an exemplary method of using any system described in this disclosure to plan implantation of a stent include providing feedback to an operator to ensure that at least an ultrasound sensor has been positioned and orientated to capture frames and views that are in sync with a stent instruction for user (IFU). In some cases, the exemplary method may additionally include determining, using a placement and size determination engine, one or more of placement, including position and orientation, and size of a stent. Yet another aspect includes another exemplary method of using any system described in this disclosure to guide implantation of a stent. In still yet another aspect, yet another exemplary method of using any system described in this disclosure post-implantation of a stent includes visualization of actual implanted stent device, using a post-implantation three-dimensional (3D) reconstruction model. In some cases, the yet another exemplary method additionally includes displaying, using at least a display, the 3D constructed image of the actual implanted stent and the 3D stent model overlaid, with recommended stent placement. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. Referring now to FIG.1, a block diagram of an exemplary system 100 for transesophageal echocardiogram guided implantation of a stent is illustrated. System 100 includes at least a transesophageal echocardiogram (TEE) system 102. For the purposes of this disclosure, a “transesophageal echocardiography (TEE) system” is a system designed to perform transesophageal echocardiography. For the purposes of this disclosure, “transesophageal echocardiography” is a medical imaging technique in which an ultrasound transducer is inserted into the esophagus to produce images of the heart and surrounding structures. In some embodiments, TEE system 102 may include a combination of specialized hardware and software designed to facilitate transesophageal echocardiography by positioning an ultrasound probe (e.g., 14 Attorney Docket No.1518-194PCT1 ultrasound transducer) within esophagus of a patient, close to the patient’s heart. As the esophagus is proximate to the heart, a TEE system 102 can detect ultrasound image 104 of cardiac tissue of a patient. In some embodiments, TEE system 102 may include a TEE probe (endoscope). The TEE probe is a long, flexible device equipped with an ultrasound transducer (ultrasound sensor 106) at its tip. Ultrasound transducer can emit high-frequency sound waves and capture the echoes reflected from cardiac structures to produce detailed images (ultrasound image 104). In a non-limiting example, TEE probe may be inserted into the patient’s esophagus, where the TEE probe may be connected to an ultrasound machine, which processes signals from ultrasound transducer to generate images of the heart. In some embodiments, TEE system 102 may be communicatively connected to at least a display 108. The display disclosed herein is further described in detail below. Additional disclosure related to TEE system 102 is further described in detail with respect to FIG.5. With continued reference to FIG.1, TEE system 102 includes at least an ultrasound sensor 106 configured to be located within an esophagus of a patient and detect a plurality of ultrasound images 104 as a function of cardiac tissue of the patient. For the purposes of this disclosure, an “ultrasound sensor” is a device that uses ultrasonic waves. In a non-limiting example, ultrasound sensor 106 may measure the distance to an object using ultrasonic sound waves. For the purposes of this disclosure, a “sensor” is a device that produces an output signal for the purpose of sensing a physical phenomenon. For example, and without limitation, sensor may transduce a detected phenomenon, such as without limitation, temperature, voltage, current, pressure, speed, motion, light, moisture, sound waves, and the like, into a sensed signal. Sensor may output the sensed signal. Sensor may include any computing device as described in the entirety of this disclosure and configured to convert and / or translate a plurality of signals detected into electrical signals for further analysis and / or manipulation. Electrical signals may include analog signals, digital signals, periodic or aperiodic signal, step signals, unit impulse signal, unit ramp signal, unit parabolic signal, signum function, exponential signal, rectangular signal, triangular signal, sinusoidal signal, sinc function, or pulse width modulated signal. Any datum captured by sensor may include circuitry, computing devices, electronic components or a combination thereof that translates into at least an electronic signal configured to be transmitted to another electronic component. In a non-limiting embodiment, sensor may include a plurality of sensors included in a sensor suite. In one or more embodiments, and without limitation, sensor 15 Attorney Docket No.1518-194PCT1 may include a plurality of sensors. Sensor may include an ultrasound sensor 106. With continued reference to FIG.1, in some embodiments, ultrasound sensor 106 may include an electrode. For the purposes of this disclosure, an “electrode” is a conductive material or element that facilitates the transmission and reception of electrical signals associated with ultrasound waves. In a non-limiting example, electrode may detect and record electrical activity; for instance, but not limited to, the heart's electrical signals. For example, and without limitation, electrode may generate ultrasonic sound waves, from which ultrasound sensor 106 receives the ultrasonic waves and transmit ultrasound image 104 related to the ultrasonic waves to processor 110. In some embodiments, ultrasound sensor 106 may include a transducer. For the purposes of this disclosure, a “transducer” is a component of an ultrasound sensor that converts one form of energy into another. In a non-limiting example, transducer may operate on a principle of piezoelectricity, where piezoelectric material can convert electrical energy into mechanical vibration (i.e., ultrasonic waves) and vice versa. In some embodiments, ultrasound sensor 106 may include a transceiver. For the purposes of this disclosure, a “transceiver” is a combined unit of a transmitter and a receiver. In a non-limiting example, transceiver may transmit ultrasonic waves and receive echoes. With continued reference to FIG.1, for the purposes of this disclosure, an “ultrasound image” is a visual representation generated by reflection of high-frequency sound waves off internal body structures. In a non-limiting example, ultrasound image 104 may include visual representation of a heart examined through esophagus. As a non-limiting example, ultrasound image 104 may include distance between sensor and surrounding tissue or organs. In some cases, ultrasound sensor 106 may detect ultrasound image 104 in a plurality of angles. In a non-limiting example, ultrasound image 104 may include a plurality of distances between sensor and a heart in different angles. For example, and without limitation, when ultrasound sensor 106 moves around within an esophagus, ultrasound sensor 106 receives a plurality of distances between ultrasound sensor 106 and organ and generate ultrasound image 104 using the plurality of distances. As another non-limiting example, ultrasound sensor 106 may include signal strength or amplitude of ultrasonic signal emitted and received by ultrasound sensor 106, images within an organ, or the like. As another non-limiting example, ultrasound sensor 106 may include ambient temperature, humidity, atmospheric pressure, or the like. In some embodiments, ultrasound sensor 106 may be stored in a database. In some embodiments, ultrasound image 104 16 Attorney Docket No.1518-194PCT1 may be retrieved from database. In some embodiments, user may manually input ultrasound image 104. In some embodiments, ultrasound image 104 may be received from remote device. As a non-limiting example, processor 110 may receive ultrasound sensor 106 from a computing device or processor incorporated with ultrasound sensor 106 or TEE system 102. With continued reference to FIG.1, system 100 includes a computing device 112. Computing device 112 includes a processor 110 communicatively connected to a memory 114. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment or linkage between two or more relata which allows for reception and / or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and / or transmittance of data and / or signal(s) therebetween. Data and / or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and / or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device 112. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. With continued reference to FIG.1, in some embodiments, computing device 112 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and / or system on a chip (SoC) as described in this disclosure. Computing device 112 may include, be included in, and / or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 112 may include a single computing device operating independently, or may include two or more 17 Attorney Docket No.1518-194PCT1 computing devices operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Computing device 112 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device 112 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and / or from a computer and / or a computing device 112. Computing device 112 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Computing device 112 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 112 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Computing device 112 may be implemented, as a non- limiting example, using a “shared nothing” architecture. With continued reference to FIG.1, in some embodiments, computing device 112 may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device 112 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or outputs of repetitions to produce an 18 Attorney Docket No.1518-194PCT1 aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device 112 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing. With continued reference to FIG.1, memory 114 contains instructions configuring processor 110 to receive a plurality of ultrasound images 104. As a non-limiting example, ultrasound image 104 may include a visualization of a whole or a portion of a heart (e.g., before a stent placement). As another non-limiting example, ultrasound image 104 may include a visualization of a catheter within a heart (e.g., during a stent placement). As another non-limiting example, ultrasound image 104 may include a visualization of a heart with a stent (e.g., after a stent placement). In some embodiments, processor 110 may receive ultrasound image 104 from TEE system 102. In some embodiments, processor 110 may receive ultrasound image 104 from a stent database 116. In some embodiments, system 100 may include a stent database 116. As used in this disclosure, a “stent database” is a data structure configured to store data associated with a stent. In one or more embodiments, stent database 116 may include inputted or calculated information and datum related to stent. In some embodiments, a datum history may be stored in stent database 116. As a non-limiting example, the datum history may include real-time and / or previous inputted data related to stent. As a non-limiting example, stent database 116 may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, where the instructions may include examples of the data related to stent. With continued reference to FIG.1, in some embodiments, processor 110 may be communicatively connected with stent database 116. For example, and without limitation, in some cases, stent database 116 may be local to processor 110. In another example, and without limitation, stent database 116 may be remote to processor 110 and communicative with processor 19 Attorney Docket No.1518-194PCT1 110 by way of one or more networks. The network may include, but is not limited to, a cloud network, a mesh network, and the like. By way of example, a “cloud-based” system can refer to a system which includes software and / or data which is stored, managed, and / or processed on a network of remote servers hosted in the “cloud,” e.g., via the Internet, rather than on local severs or personal computers. A “mesh network” as used in this disclosure is a local network topology in which the infrastructure processor 110 connect directly, dynamically, and non-hierarchically to as many other computing devices as possible. A “network topology” as used in this disclosure is an arrangement of elements of a communication network. The network may use an immutable sequential listing to securely store stent database 116. An “immutable sequential listing,” as used in this disclosure, is a data structure that places data entries in a fixed sequential arrangement, such as a temporal sequence of entries and / or blocks thereof, where the sequential arrangement, once established, cannot be altered or reordered. An immutable sequential listing may be, include and / or implement an immutable ledger, where data entries that have been posted to the immutable sequential listing cannot be altered. With continued reference to FIG.1, in some embodiments, stent database 116 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and / or data structure, such as a distributed hash table or the like. Database may include a plurality of data entries and / or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and / or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and / or reflect data and / or records as used herein, as well as categories and / or populations of data consistently with this disclosure. With continued reference to FIG.1, in some embodiments, processor 110 may receive ultrasound image 104 from a user device 118. For the purposes of this disclosure, a “user device” is any device a user use to input data. For the purposes of this disclosure, a “user” is an individual or entity that uses an apparatus. As a non-limiting example, a user may include a 20 Attorney Docket No.1518-194PCT1 surgeon, doctor, medical professional, and the like. As a non-limiting example, user device 118 may include a laptop, desktop, tablet, mobile phone, smart phone, smart watch, kiosk, screen, smart headset, or things of the like. In some embodiments, user device 118 may include an interface configured to receive inputs from user. In some embodiments, user may manually input any data into computing device 112 using user device 118. In some embodiments, user may have a capability to process, store or transmit any information independently. With continued reference to FIG.1, processor 110 may be configured to extract at least a cardiac featuring datum 120 and at least a catheter featuring datum 122 from a plurality of ultrasound images 104. As used in this disclosure, “cardiac featuring datum” is a characteristic or attribute related to a heart. In some embodiments, cardiac featuring datum 120 may include spatial arrangement, shape, size, or texture of a heart. In some cases, cardiac featuring datum 120 may include one or more embedded values described herein and their combinations thereof. In a non-limiting example, cardiac featuring datum 120 may be represented numerically as a vector, a metric or other mathematical constructs that capture specific spatial characteristics. In some cases, cardiac featuring datum 120 may also be visualized as contours, surfaces, or other geometric representations. As a non-limiting example, cardiac featuring datum 120 may include structural elements like coronary artery, vein, and the like. In some embodiments, extracting a cardiac featuring datum 120 may include isolating and identifying cardiac featuring datum 120 from ultrasound image 104 using image processing or machine learning techniques. In an embodiment, cardiac featuring datum 120 may be extracted using edge detection, texture analysis, or other image processing techniques (e.g., cleaning and enhancing images, image segmentation, and / or the like). In another embodiment, one or more machine learning models, such as convolutional neural networks (CNNs) as described in further detail below, may be used to extract complex cardiac featuring datum 120. Still referring to FIG.1, in a non-limiting example, one or more cardiac featuring datum 120 may include one or more shape features (i.e., characteristics related to the shape of specific cardiac structures), such as curvature, surface area, volume, and / or the like. In another non- limiting example, one or more cardiac featuring datum 120 may include one or more texture features (i.e., characteristics related to the texture or pattern within cardiac tissues, as seen ultrasound image 104), such as gray-level co-occurrence matrix (GLCM) features representing the texture of heart muscle tissue. In another non-limiting example, one or more cardiac featuring 21 Attorney Docket No.1518-194PCT1 datum 120 may include one or more orientation features (i.e., characteristics related to the orientation or alignment of cardiac structures), such as the angle or alignment of the septum within the heart. In a further non-limiting example, one or more cardiac featuring datum 120 may include one or more edge and boundary features (i.e., characteristics related to the edges or boundaries between different cardiac structures or tissues), such as edge detection features highlighting the boundary between the myocardium and the cardiac chambers. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various cardiac feature datums extracted from ultrasound image 104 in consistent with this disclosure. With continued reference to FIG.1, as used in this disclosure, a “catheter featuring datum” is a characteristic or attribute related to a catheter. For the purposes of this disclosure, a “catheter” is a medical device designed to be inserted into a body to facilitate diagnostic, therapeutic, or interventional procedures. Catheters may include biocompatible materials such as polyurethane, silicone, or polyethylene and may include various sizes, lengths, and configurations. A catheter can be inserted into a coronary artery with an empty balloon and a stent attached to the end. Without limitation the stent may include a medical device designed to be implanted within a bodily lumen to restore or maintain openness by providing structural support. Stents may include tubular in shape and may include biocompatible materials such as stainless steel, cobalt-chromium alloys, nitinol (a shape-memory alloy), or biodegradable polymers. With continued reference to FIG.1, in some embodiments, catheter featuring datum 122 may include spatial arrangement, shape, size, or material properties of a catheter. In some cases, catheter featuring datum 122 may include one or more embedded values described herein and their combinations thereof. In a non-limiting example, catheter featuring datum 122 may be represented numerically as a vector, a metric, or other mathematical constructs that capture specific structural or positional characteristics. In some cases, catheter featuring datum 122 may also be visualized as trajectories, surfaces, or other geometric representations. As a non-limiting example, catheter featuring datum 122 may include positional data, curvature, or anchoring elements of a catheter. In some embodiments, extracting a catheter featuring datum 122 may include isolating and identifying catheter featuring datum 122 from ultrasound image 104, fluoroscopic imaging, or other imaging modalities using image processing or machine learning techniques. In an embodiment, catheter featuring datum 122 may be extracted using edge 22 Attorney Docket No.1518-194PCT1 detection, curvature analysis, or other image processing techniques (e.g., cleaning and enhancing images, image segmentation, and / or the like). In another embodiment, one or more machine learning models, such as convolutional neural networks (CNNs) as described in further detail below, may be used to extract complex catheter featuring datum 122. With continued reference to FIG.1, in some embodiments, processor 110 may be configured to analyze ultrasound image 104 using machine vision system to extract cardiac featuring datum 120 and / or catheter featuring datum 122. For the purposes of this disclosure, a “machine vision system” is a type of technology that enables a computing device to inspect, evaluate and identify still or moving images. For example, in some cases a machine vision system may be used for world modeling or registration of objects within a space. In some cases, registration may include image processing, such as without limitation object recognition, feature detection, edge / corner detection, and the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, a machine vision process may operate image classification and segmentation models, such as without limitation by way of machine vision resource (e.g., OpenMV or TensorFlow Lite). A machine vision process may detect motion, for example by way of frame differencing algorithms. A machine vision process may detect markers, for example blob detection, object detection, face detection, and the like. In some cases, a machine vision process may perform eye tracking (i.e., gaze estimation). In some cases, a machine vision process may perform person detection, for example by way of a trained machine learning model. In some cases, a machine vision process may perform motion detection (e.g., camera motion and / or object motion), for example by way of optical flow detection. In some cases, machine vision process may perform code (e.g., barcode) detection and decoding. In some cases, a machine vision process may additionally perform image capture and / or video recording. With continued reference to FIG.1, in some cases, registration may include one or more transformations to orient a camera frame (or an image or video stream) relative a three- dimensional coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration of first frame to a coordinate system may be verified and / or corrected using object identification and / or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed 23 Attorney Docket No.1518-194PCT1 using a two-dimensional projection of points in three dimensions onto a first frame, however. A third dimension of registration, representing depth and / or a z axis, may be detected by comparison of two frames; for instance, where first frame includes a pair of frames captured using a pair of cameras (e.g., stereoscopic camera also referred to in this disclosure as stereo- camera), image recognition and / or edge detection software may be used to detect a pair of stereoscopic views of images of an object; two stereoscopic views may be compared to derive z- axis values of points on object permitting, for instance, derivation of further z-axis points within and / or around the object using interpolation. This may be repeated with multiple objects in field of view, including without limitation environmental features of interest identified by object classifier and / or indicated by an operator. In an embodiment, x and y axes may be chosen to span a plane common to two cameras used for stereoscopic image capturing and / or an xy plane of a -populated in translational and rotational matrices, for affine transformation of coordinates of object, also as described above. Initial x and y coordinates and / or guesses at transformational matrices may alternatively or additionally be performed between first frame and second frame, as described above. For each point of a plurality of points on object and / or edge and / or edges of object as described above, x and y coordinates of a first stereoscopic frame may be populated, with an initial estimate of z coordinates based, for instance, on assumptions about object, such as an assumption that ground is substantially parallel to an xy plane as selected above. Z coordinates, and / or x, y, and z coordinates, registered using image capturing and / or object identification processes as described above may then be compared to coordinates predicted using initial guess at transformation matrices; an error function may be computed using by comparing the two sets of points, and new x, y, and / or z coordinates, may be iteratively estimated and compared until the error function drops below a threshold level. With continued reference to FIG.1, alternatively or additionally, identifying cardiac featuring datum 120 and / or catheter featuring datum 122 may include classifying cardiac featuring datum 120 and / or catheter featuring datum 122 to a label of the cardiac featuring datum 120 and / or catheter featuring datum 122 using an image classifier; the image classifier may be trained using a plurality of images of cardiac featuring datum 120 and / or catheter featuring datum 122. The image classifier may be configured to determine which of a plurality of edge- detected shapes is closest to an attribute set of cardiac featuring datum 120 and / or catheter 24 Attorney Docket No.1518-194PCT1 featuring datum 122 as determined by training using training data and selecting the determined shape as the cardiac featuring datum 120 and / or catheter featuring datum 122. As a non-limiting example, the image classifier may be trained with image training data that correlates the plurality of images of cardiac featuring datum 120 and / or catheter featuring datum 122 to a label of the cardiac featuring datum 120 and / or catheter featuring datum 122. Alternatively, identification of the cardiac featuring datum 120 and / or catheter featuring datum 122 may be performed without using computer vision and / or classification; for instance, identifying the cardiac featuring datum 120 and / or catheter featuring datum 122 may further include receiving, from a user, an identification of the cardiac featuring datum 120 and / or catheter featuring datum 122 in an ultrasound image 104. With continued reference to FIG.1, in some embodiments, extracting at least a cardiac featuring datum 120 and / or catheter featuring datum 122 may include segmenting the at least an ultrasound image 104 into a plurality of image segments 124. As a non-limiting example, cardiac featuring datum 120 may include structural elements like coronary artery of a heart, and the like. In some embodiments, extracting a cardiac featuring datum 120 and / or catheter featuring datum 122 may include isolating and identifying cardiac featuring datum 120 and / or catheter featuring datum 122 from ultrasound image 104 using image processing or machine learning techniques. For the purposes of this disclosure, an “image segment” is a section of an ultrasound image. In some embodiments, image segment 124 may include a section of an ultrasound image that has been partitioned based on shared visual or anatomical properties (e.g., cardiac featuring datum 120 and / or catheter featuring datum 122). In a non-limiting example, in a 2D image, segmenting ultrasound image 104 may include all the pixels that make up cardiac featuring datum 120 and / or catheter featuring datum 122, while in a 3D context, it would encompass all the voxels (3D pixels) that constitute cardiac featuring datum 120 and / or catheter featuring datum 122. In some embodiments, processor 110 may segment three-dimensional (3D) cardiac model 126 and segmenting 3D cardiac model 126 may include extracting cardiac featuring datum 120 and / or catheter featuring datum 122 from 3D cardiac model 126 and segmenting 3D cardiac model 126 as a function of cardiac featuring datum 120 and / or catheter featuring datum 122. With continued reference to FIG.1, in some embodiments, segmenting an ultrasound image 104 and / or 3D cardiac model 126 may include training a segmentation model with segmentation training data, wherein the segmentation training data may include exemplary 25 Attorney Docket No.1518-194PCT1 ultrasound image and / or 3D cardiac model correlated to exemplary segmented ultrasound image and / or 3D cardiac model and segmenting a ultrasound image 104 and / or 3D cardiac model 126 using the trained segmentation model. For the purposes of this disclosure, a “segmentation model” is a machine learning or deep learning model designed to partition an image into multiple segments or regions, each corresponding to different objects or parts of an object within the image. In some embodiments, segmentation model may assign a label to each pixel in an image (e.g., ultrasound image 104 and / or 3D cardiac model 126) such that pixels with the same label share certain characteristics, such as belonging to the same cardiac featuring datum 120 and / or catheter featuring datum 122 or region. As a non-limiting example, segmentation model may include a neural network. For the purposes of this disclosure, “segmentation training data” is data containing correlations that a machine-learning process may use to model relationships between echo depth maps and segmented echo depth maps. In a non-limiting example, a segmentation model may analyze ultrasound image 104 and / or 3D cardiac model 126 to identify and delineate the boundaries of cardiac featuring datum 120 and / or catheter featuring datum 122. This mayinclude finding the set of coordinates {( , )} that represent the pixels or voxels making up thecardiac featuring datum 120 and / or catheter featuring datum 122. In some embodiments, processor 110 may segment ultrasound image and / or 3D cardiac model 126 based on cardiac featuring datum 120 and / or catheter featuring datum 122. In some embodiments, segmentation training data may be stored in Stent database 116. In some embodiments, segmentation training data may be received from one or more users, Stent database 116, external computing devices, and / or previous iterations of processing. As a non-limiting example, segmentation training data may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, which may be stored in memory and / or stored in Stent database 116, where the instructions may include labeling of training examples. In some embodiments, segmentation training data may be updated iteratively on a feedback loop. As a non-limiting example, processor 110 may update segmentation training data iteratively through a feedback loop as a function of output of feature extraction model, ultrasound image 104, 3D cardiac model 126, and the like. In some embodiments, processor 110 may be configured to generate segmentation model. In a non-limiting example, generating segmentation model may include training, retraining, or fine-tuning segmentation model using segmentation training data or updated segmentation training data. In some embodiments, processor 110 may be configured to 26 Attorney Docket No.1518-194PCT1 segment ultrasound image 104 and / or 3D cardiac model 126 using segmentation model (i.e., trained or updated segmentation model). With continued reference to FIG.1, memory 114 contains instructions configuring processor 110 to generate at least a three-dimensional (3D) cardiac model 126 representative of a heart of a patient. In some embodiments, processor 110 may be configured to generate at least a 3D catheter model 128 within the at least a 3D cardiac model 126 as a function of at least a cardiac featuring datum 120 and at least a catheter featuring datum 122, wherein the at least a 3D catheter model 128 is representative of a catheter with a stent. For the purposes of this disclosure, a “three-dimensional cardiac model” is a three-dimensional representation of a patient’s heart and / or surrounding structures. In some embodiments, 3D cardiac model may include peripheral vasculature. “Peripheral vasculature,” for the purposes of this disclosure, is a structure or structures of blood vessels surrounding or in the immediate vicinity of the heart. For the purposes of this disclosure, a “three-dimensional catheter model” is a three-dimensional representation of a catheter.3D catheter model 128 is a representative of a catheter containing a balloon and a stent. In a non-limiting example, 3D cardiac model 126 and / or 3D catheter model 128 may include a 3D voxel occupancy representation (VOR). As used in this disclosure, a “3D voxel occupancy representation (VOR)” is a 3D digital representation of a spatial structure of an object, wherein the representation is composed of a plurality of discrete volumetric elements known as voxels. A “voxel,” for the purpose of this disclosure, is a 3D equivalent of a pixel in 2D imaging. While a pixel represents a point in a 2D image and may include properties such as color and / or brightness, a voxel may represent a volume in a 3D space and may include additional properties such density / occupancy as described below. In an embodiment, each voxel of plurality of voxels within 3D VOR may represent a specific portion of heart. In some cases, voxel may be a smallest distinguishable box-shaped part (i.e., 1px·1px·1px) of a three- dimensional image. In some cases, each voxel of plurality of voxels within VOR may be represented as a cube or rectangular prism (although other shapes may be used in specialized applications). Each voxel may include a size that determines a resolution of the 3D image or model. In an embodiment, smaller voxels may provide higher resolution; however, it may require more computational resources (e.g., RAM) for processor 110 to process. In an embodiment, and still referring to FIG.1, each voxel of plurality of voxels within VOR may include one or more embedded values. As used herein, “embedded values” refers to 27 Attorney Docket No.1518-194PCT1 specific numerical or categorical data associated with each voxel. In some cases, embedded values may represent various attributes or characteristics of the corresponding portion of heart that voxel represents. In a non-limiting example, embedded values may include density values, intensity values, texture information, or any other quantitative measures that provide insights into the underlying cardiac tissue. Such embedded values may be derived from set of ultrasonic images or other imaging modalities used to generate 3D cardiac model 126 and / or 3D catheter model 128. In some cases, embedded values may be utilized, by processor 110, to differentiate between different types of cardiac tissues, such as myocardial tissue, blood vessels, or chambers. Embedded values may also facilitate the visualization of dynamic cardiac functions, for example, and without limitation, blood flow or heart beating by encoding temporal information such as timestamps within plurality of voxels. In some cases, and still reference to FIG.1, one or more embedded values, such as, without limitations, occupancy, or density, may be derived from ultrasound images 104 described herein by processor 110. In a non-limiting example, determining occupancy status of each voxel of plurality of voxels may include converting set of ultrasonic images 104 to a set of binary images and determining occupancy status of each voxel as a function of the structure of interest’s binary value. In some cases, occupancy status may include a value representing the likelihood of occupancy of the corresponding heart tissue. In another non-limiting example, density may be calculated, by processor 110, for each voxel as a function of the echogenicity of one or more pixels on a given ultrasound image 104, wherein, the brightness of the given ultrasonic image may be analyzed since different tissues reflect ultrasound waves differently. With continued reference to FIG.1, generating 3D cardiac model 126 and / or 3D catheter model 128 may include generating a 3D array. In some cases, processor 110 may divide 3D space into a grid of plurality of voxels, each with specific x, y, and z coordinates as embedded values. Each element of 3D array may correspond to a voxel. In some cases, 3D array may allow for easy access and manipulation of plurality of voxels, enabling various analyses, visualizations, and transformations either described or not described herein. In a non-limiting example, embedded values may include a density of the tissue at a specific location of a patient’s body derived from one or more ultrasonic images of ultrasound images 104. Additionally, or alternatively, and still referring to FIG.1, 3D cardiac model 126 and / or 3D catheter model 128 may include a 3D grid embedded values described herein of plurality of 28 Attorney Docket No.1518-194PCT1 voxels (e.g., tissue density, blood flow velocity, echogenicity or acoustic properties, and any other biophysical properties). As used in this disclosure, a “3D grid” refers to a 3D data structure that divides a given volume (e.g., volume of a heart) into a plurality of discrete units called cells (i.e., volume elements). In an embodiment, each cell within 3D grid may be associated with a distinct voxel. In yet another embodiment, and still referring to FIG.1, cells may be continuous, meaning that one or more cells may represent one or more continuous regions of space rather than discreate, separate units. In a non-limiting example, instead of being uniform, mapped presence indicator and / or other embedded values may vary continuously across different cells or cell’s volume. In such embodiment, processor 110 may use interpolation to estimate other (unknown) embedded values within a range based on existing values such as known embedded values at specific points, thereby allowing for smooth transitions between cells. Exemplary interpolation methods may include, without limitation, linear interpolation, cubic interpolation, and / or the like. For example, and without limitation, if the corners of a cell have known values interpolation can be used to estimate the values at any point within the cell based on those corner values. In a non-limiting example, and still referring to FIG.1, 3D cardiac model 126 and / or 3D catheter model 128 may include a 3D grid having a plurality of cells e.g., voxels, wherein each cell may contain a continuous range of values representing tissue density, blood flow velocity, or other properties (i.e., embedded values). Processor 110 may be configured to apply trilinear or tricubic interpolation to estimate tissue density within each cell based on presence indicator or other known values at the cell’s boundaries, since tissue densities change gradually; Such 3D grid may provide a smooth, continuous representation of heat’s internal structures, allowing for more nuanced analysis and visualization as described below. In a further embodiment, 3D grid with continuous cells may be additionally used in fluid dynamics simulations. With continued reference to FIG.1, in some case, embedded values may be mapped to 3D grid as a function of array masking, wherein specific array or grid may be selected to modify based on one or more pre-defined criteria. In a non-limiting example, processor 110 may generate a mask e.g., a binary array that defines which voxels or cells are affected. Mask may be used to select or modify specific voxels or cells based on certain attributes; for instance, and without limitation, processor 110 may use mask to isolate the left atrium (LA) within the heart 29 Attorney Docket No.1518-194PCT1 focusing the analysis on that specific region. Such mask may include a criteria defined by specific density thresholds that distinguish the LA’s tissue (i.e., voxels representing coronary artery in 3D grid) from surrounding structures (i.e., neighboring voxels). In some cases, such mask may further include a binary mask, wherein each voxel in the 3D gird may be assigned a first presence indicator such as 1 if the voxel meets the criteria for the coronary artery and a second presence indicator such as 0 if it does not. In some embodiments, mask may be directly applied to 3D grid, selecting, or modifying voxels or cells, thereby enabling processor 110 to highlight, exclude, or otherwise manipulate specific parts of heart within 3D grid. Processor 110 may then perform an element-wise multiplication between 3D grid and the mask. Continuing from the previous non-limiting example, voxels corresponding to the coronary artery (wherein the mask value is 1) may retain their original values, while other voxels (where the mask value is 0) may be set to 0 or other specific value (i.e., excluded or masked out). In some embodiments, 3D grid may include one or more cardiac featuring datum 120 and / or catheter featuring datum 122 extracted from ultrasound image 104 of heart. Still referring to FIG.1, as used in this disclosure, a “vector” is a data structure that represents one or more a quantitative values and / or measures of one or more cardiac featuring datum 120 and / or catheter featuring datum 122. A vector may be represented as an n-tuple of values, where n is one or more values, as described in further detail below; a vector may alternatively or additionally be represented as an element of a vector space, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and / or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting 30 Attorney Docket No.1518-194PCT1 example, a vector represented as [5, 10, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent, for instance as measured using cosine similarity as computed using a dot product of two vectors; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute l as derived using aPythagorean norm: = , where ai is attribute number i of the vector. Scaling and / ornormalization may vector comparison independent of absolute quantities of attributes, while any on similarity of attributes. With continued reference to FIG.1, in some embodiments, system 100 may include a computer vision model configured to generate 3D cardiac model 126 and / or 3D catheter model 128. A “computer vision model,” for the purpose of this disclosure, is a computation model designed to interpret and make determinations based on visual data. In an embodiment, computer vision model may process ultrasound images 104, to make a determination about a scene, space, and / or object in heart. In a non-limiting example, computer vision model may be used for registration of plurality of voxels within a 3D space. In some cases, registration may include image processing described herein, such as without limitation object recognition, feature detection, edge / corner detection, and the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, registration may include one or more transformations to orient an ultrasonic image relative a 3D coordinate system; exemplary transformations include without limitation, homography transforms and affine transforms. In an embodiment, registration of ultrasonic image to a coordinate system may be verified and / or corrected using object identification and / or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed using a two-dimensional projection of points in three dimensions onto the ultrasonic image; however, a third dimension of registration, representing depth and / or a 31 Attorney Docket No.1518-194PCT1 z axis, may be detected by utilizing depth-sensing techniques such as Doppler imaging. Alternatively, the third dimension may be inferred from the known geometry and orientation of the imaging device (e.g., ultrasound sensor 106 of TEE system 102), or through the application of one or more machine learning models trained to interpret depth from the two-dimensional projection. With continued reference to FIG.1, processor 110 may use a machine learning module to implement one or more algorithms or generate one or more machine learning models, such as an cardiac modeling model to generate 3D cardiac model 126 and / or 3D catheter model 128. However, the machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine-learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that a machine- learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and / or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and / or user inputs and outputs correlated to each of those inputs so that a machine-learning model may determine an output. Correlations may indicate causative and / or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below. In one or more embodiments, training data may be formatted and / or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a non- limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements. In a further embodiment, training data may include previous outputs such that one or more machine learning models iteratively produces 32 Attorney Docket No.1518-194PCT1 outputs. Still referring to FIG.1, machine learning module may be used to generate anatomy modeling model and / or any other machine learning models, such as, shape identification model as described in further detail below, using training data. Cardiac modeling model may be trained by correlated inputs and outputs of training data. Training data may be data sets that have already been converted from raw data whether manually, by machine, or any other method. In an embodiment, generating 3D cardiac model 126 and / or 3D catheter model 128 may include receiving anatomy training data, wherein the anatomy training data may include a plurality of ultrasound images as input and a plurality of 3D cardiac models and 3D catheter models as output. In some cases, anatomy training data may be received from Stent database 116 or other databases. In other cases, anatomy training data may be collected by a data acquisition unit from external sources such as one or more medical equipment’s e.g., imaging devices or diagnostic tools, wherein the data acquisition may be configured as an intermediary between the data source and machine learning module. In one or more embodiments, anatomy training data may include a plurality of ultrasound images correlated to a plurality of 3D cardiac models and 3D catheter models. In one or more embodiments, a particular ultrasound images 104 within anatomy training data may be correlated to a particular 3D cardiac model and 3D catheter model. In one or more embodiments, anatomy training data may further include a plurality of ultrasound images 104 correlated to a plurality of cardiac models and 3D catheter models. In an embodiment, a particular ultrasound images 104 may be correlated to a particular cardiac model and 3D catheter model. In one or more embodiments anatomy training data may include TEE diagrams, Cardiac CTs, ECG signals and / or ultrasonic images as an input and correlated 3D representations of heart and catheter. With continued reference to FIG.1, in an embodiment, anatomy modeling model may include a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. Neural network is described in further detail below with reference to FIGS.6 - 8 In a non-limiting example, anatomy modeling model may include a convolutional neural network (CNN). Generating 3D cardiac model 126 and / or 3D catheter model 128 may include training CNN using anatomy training data and generating 3D cardiac model 126 and / or 3D catheter model 128 as a function of ultrasound images 104 using trained CNN. A “convolutional neural network,” for the purpose of this 33 Attorney Docket No.1518-194PCT1 disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., ultrasound images 104 through a sliding window approach. In some cases, convolution operations may enable processor 110 to detect local / global patterns, edges, textures, and any other cardiac featuring datum 120 and / or catheter featuring datum 122 described herein within each ultrasound images 104. Cardiac featuring datum 120 and / or catheter featuring datum 122 may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non- linearities into the processing step of generating 3D cardiac model 126 and / or 3D catheter model 128. Additionally, or alternatively, CNN may also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non-limiting example, CNN may include one or more pooling layer configured to reduce the spatial dimensions of cardiac featuring datum and / or catheter featuring datum maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more cardiac featuring datum 120 and / or catheter featuring datum 122. Still referring to FIG.1, CNN may further include one or more fully connected layers configured to combine cardiac featuring datum 120 and / or catheter featuring datum 122 extracted by the convolutional and pooling layers. In some cases, one or more fully connected layers may allow for higher-level pattern recognition. In a non-limiting example, one or more fully connected layers may connect every neuron (i.e., node) in its input to every neuron in its output, functioning as a traditional feedforward neural network layer. In some cases, one or more fully connected layers may be used at the end of CNN to perform high-level reasoning and produce the final output such as, without limitation, a 3D cardiac model 126 and / or 3D catheter model 128. Further, each fully connected layer may be followed by one or more dropout layers configured to prevent overfitting, and one or more normalization layers to stabilize the learning process described herein. With continued reference to FIG.1, CNN may further include a 3D CNN, wherein the 3D 34 Attorney Docket No.1518-194PCT1 CNN, unlike standard 2D CNN, may include utilization of one or more 3D convolutions which allow them to directly process 3D data, thereby enabling processor 110 to generate 3D structures such as 3D cardiac model 126 and / or 3D catheter model 128 using the 3D CNN. In a non- limiting example, 3D CNN may include one or more 3D filters (i.e., kernels) that move through the ultrasound images 104 in three dimensions and capturing spatial relationships in x, y, and z axis. Similar to 3D convolutions, 3D CNN may further include one or more 3D pooling layers that may be used to reduce the dimensionality of ultrasonic images while preserving cardiac featuring datum 120 and / or catheter featuring datum 122 as described above. Additionally, or alternatively, an encoder-decoder structure may be implemented (extended to 3D), by processor 110, in 3D CNN, wherein the encoder-decoder structure includes an encoding path that captures the context and a decoding path that enables precise localization in a same manner as U-net as described above. Such encoder-decoder structures may also include a plurality of skip connections, allowing 3D CNN to use information from multiple resolutions to improve the process of generating 3D cardiac model 126 and / or 3D catheter model 128. With continued reference to FIG.1, in an embodiment, training the cardiac modeling model (i.e., CNN) may include selecting a suitable loss function to guide the training process. In a non-limiting example, a loss function that measures the difference between the predicted 3D VORs and the ground truth 3D structure e.g., CT-based anatomical object models may be used, such as, without limitation, mean squared error (MSE) or a custom loss function may be designed for one or more embodiments described herein. Additionally, or alternatively, optimization algorithms, such as stochastic gradient descent (SGD), may then be used to adjust the anatomy modeling model’s parameters to minimize such loss. In a further non-limiting embodiment, instead of directly predicting 3D cardiac model 126 and / or 3D catheter model 128, cardiac modeling model may be trained as a regression model to predict embedded values described herein for each voxel of plurality of voxels within a 3D grid. Additionally, CNN may be extended with additional deep learning techniques, such as recurrent neural networks (RNNs) or attention mechanism, to capture additional features and / or data relationships within input data. These extensions may further enhance the accuracy and robustness of the anatomical object modeling. With continued reference to FIG.1, in some embodiments, generating at least a 3D cardiac model 126 and / or 3D catheter model 128 may include generating a 3D point cloud 130 as 35 Attorney Docket No.1518-194PCT1 a function of a plurality of image segments 124 and generating a 3D mesh model 132 of at least a 3D cardiac model 126 and / or 3D catheter model 128 as a function of the 3D point cloud 130. For the purposes of this disclosure, a “three-dimensional point cloud” is a collection of data points in space, each represented by its x, y, and z coordinates. In some embodiments, 3D point cloud 130 may capture the geometry of cardiac featuring datum 120 and / or catheter featuring datum 122, providing a comprehensive 3D representation. In some embodiments, the construction of 3D point cloud 130 may integrate cardiac featuring datum 120 and / or catheter featuring datum 122 and / or image segments 124 from multiple frames. In a non-limiting example, when cardiac featuring datum 120 and / or catheter featuring datum 122 and / or image segments 124 (z) is added to pixel coordinates to convert them into 3D points (x, y, z), all the 3D points can be aggregated to form 3D point cloud 130. In some embodiments, 3D cardiac model 126 and / or 3D catheter model 128 may include 3D mesh model 132. For the purposes of this disclosure, a “three- dimensional mesh model” is a mathematical and geometric representation of the surface of a three-dimensional object. In some embodiments, 3D mesh model 132 may be constructed using a network of vertices, edges, and faces. In some embodiments, vertices may define points in 3D space, the edges may connect pairs of vertices, and the faces, in the form of triangles or quadrilaterals, may create a polygonal surface that represents the shape of the object (heart and / or catheter). In some embodiments, processor 110 may generate a mesh representing cardiac shape as a function of 3D voxel occupancy representation. Processor 110 may be configured to display, using display, a mesh to a user. With continued reference to FIG.1, in some embodiments, processor 110 may be configured to generate a three dimensional (3D) model as a function of 3D point cloud. For the purposes of this disclosure, a “three dimensional model” is a digital representation of an object of interest. As a non-limiting example, processor 110 may be configured to apply one or more 3D reconstruction algorithms, such as without limitation, marching cubes, contour detection and segmentation, active contour models, and / or the like to create a coherent 3D representation e.g., 3D model. In some cases, 3D modeling techniques may include surface modeling, solid modeling, or parametric modeling, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various 3D reconstruction algorithms that may be used by processor 110 to generate 3D model. With continued reference to FIG.1 , memory 114 contains instructions configuring 36 Attorney Docket No.1518-194PCT1 processor 110 to generate a pose datum. For the purposes of this disclosure, a “pose datum” is a data element that describes the physical stance, positioning, or orientation of an object in space. In some embodiments, pose datum may include a six degree (6D) pose datum. For the purposes of this disclosure, a “six degree pose datum” is a data element that describes an object's position and orientation in three-dimensional space using six parameters: three translational coordinates that indicate the object's location and three rotational coordinates that describe the object's orientation. In some embodiments, pose datum may include a five degree (5D) pose datum. A “five degree pose datum,” for the purposes of this disclosure, is a data element that describes an object's position and orientation in three-dimensional space using five parameters: three translational coordinates and two rotational coordinates that describe the object's orientation. In some embodiments, processor 110 may generate a 5D pose datum, opposed to a 6D pose datum, for objects that are symmetric along one of their axes. For example, a catheter may be symmetric along its longitudinal axis. For this, a 5D pose datum may be determined as it may be difficult or impossible to determine the catheter's rotation about its longitudinal axis. In a non-limiting example, in procedures involving surgical navigation, pose datum may provide information about the position and orientation of surgical instruments (e.g., inserting device) relative to the patient's anatomy. During minimally invasive surgeries, accurate pose datum can help surgeons navigate instruments with precision, reducing the risk of damaging surrounding tissues and improving surgical outcomes. For instance, in echocardiographic procedures, knowing the 6D or 5D pose of a catheter tip within the heart may allow for precise targeting of specific cardiac structures, essential for effective interventions. Similarly, accurate pose datum may aid in the diagnosis and treatment planning (e.g., anatomical datum) by providing detailed 3D models of organs, which can be invaluable for visualizing complex anatomical relationships and planning surgical approaches. In some embodiments, pose datum may be stored in echo database. In some embodiments, processor 110 may retrieve pose datum from echo database. In some embodiments, user may manually input pose datum. With continued reference to FIG.1 , in some embodiments, processor 110 may determine pose datum using various methods. In some embodiments, processor 110 may determine pose datum using instance-level 6D or 5D pose estimation by determining pose datum of object of interest using pre-existing CAD models. In a non-limiting example, processor 110 may determine pose datum using red, green, and blue (RGB)-based methods using RGB images to 37 Attorney Docket No.1518-194PCT1 estimate pose datum through various techniques. In some embodiments, RGB-based methods may include regression-based methods, template-based methods, feature-based methods, and the like. As a non-limiting example, regression-based methods may include PoseNet, PoseCNN, Deep-6DPose, YOLO-6D, and the like. PoseNet and PoseCNN may use convolutional neural networks (CNNs) to directly regress the pose datum from RGB images. These methods may predict orientation and position without intermediate keypoint representations. These methods may demonstrate the feasibility of deep learning for pose estimation but often require refinement for higher accuracy. Deep-6DPose may extend Mask R-CNN to include a pose prediction branch, simplifying the process and improving efficiency.nYOLO-6D may transform pose estimation into a keypoint regression task using the YOLO framework, offering real-time performance but limited effectiveness in complex environments. As a non-limiting example, template-based methods may include matching the input image with a set of pre-defined templates. SSD-6D and LatentFusion may use deep learning to extend traditional 2D detection networks to 3D pose estimation, leveraging large datasets of 3D shapes to improve generalization to unseen objects. DPOD may combine detection and matching using a dense matching approach, robust to occlusion and lighting changes. As another non-limiting example, feature-based methods may extract distinctive features (e.g., object feature) from the image (e.g., echo data), such as scale-invariant feature transform (SIFT) or speeded-up robust features (SURF), and may match them to corresponding features on the CAD model. These methods may extract features from images and match them with a 3D model using algorithms like Perspective- n-Point (PnP). PVNet and BB8 can employ segmentation and keypoint voting to handle occlusion and symmetry. EPOS and Pix2Pose can use deep learning to predict pixel-level 3D coordinates, improving robustness to symmetry and occlusion. Still referring to FIG.1, generating 3D data structure of a subject’s heart may include generating a 3D array. In some cases, processor 110 may divide 3D space into a grid of plurality of voxels, each with specific x, y, and z coordinates as embedded values. Each element of 3D array may correspond to a voxel. In some cases, 3D array may allow for easy access and manipulation of plurality of voxels, enabling various analyses, visualizations, and transformations either described or not described herein. In a non-limiting example, embedded values may include a density of the tissue at a specific location of a patient’s body derived from one or more ultrasonic images of plurality of ultrasonic images 104. 38 Attorney Docket No.1518-194PCT1 Still referring to FIG.1, 3D data structure of structure may include a 3D grid configured to map presence indicators and / or other embedded values described herein of plurality of voxels (e.g., tissue density, blood flow velocity, echogenicity or acoustic properties, and any other biophysical properties). As used in this disclosure, a “3D grid” refers to a 3D data structure that divides a given volume (e.g., volume of a structure) into a plurality of discrete units called cells (i.e., volume elements). In an embodiment, each cell within 3D grid may be associated with a distinct voxel. Mapping presence indicators or other embedded values may include assigning each presence indicator or embedded value to each point within 3D grid such as corners of each corresponding cell. Such values may be derived from plurality of ultrasonic images 104 as described above. Still referring to FIG.1, cells may be continuous, meaning that one or more cells may represent one or more continuous regions of space rather than discreate, separate units. In a non- limiting example, instead of being uniform, mapped presence indicator and / or other embedded values may vary continuously across different cells or cell’s volume. In such embodiment, processor 110 may use interpolation to estimate other (unknown) embedded values within a range based on existing values such as known embedded values at specific points, thereby allowing for smooth transitions between cells. Exemplary interpolation methods may include, without limitation, linear interpolation, cubic interpolation, and / or the like. For example, and without limitation, if the corners of a cell have known values interpolation can be used to estimate the values at any point within the cell based on those corner values. Still referring to FIG.1, 3D data structure of a heart may include a 3D grid having a plurality of cells e.g., voxels, wherein each cell may contain a continuous range of values representing tissue density, blood flow velocity, or other properties (i.e., embedded values). Processor 110 may be configured to apply trilinear or tricubic interpolation to estimate tissue density within each cell based on presence indicator or other known values at the cell’s boundaries, since tissue densities change gradually; Such 3D grid may provide a smooth, continuous representation of heat’s internal structures, allowing for more nuanced analysis and visualization as described below. In a further embodiment, 3D grid with continuous cells may be additionally used in fluid dynamics simulations. Still referring to FIG.1, presence indicators and / or other embedded values may be mapped to a 3D grid as a function of array masking. In a non-limiting example, processor 110 39 Attorney Docket No.1518-194PCT1 may generate a mask e.g., a binary array that defines which voxels or cells are affected. Mask may be used to select or modify specific voxels or cells based on certain attributes; for instance, and without limitation, processor 110 may use a mask to isolate the LA within the heart focusing the analysis on that specific region. Such mask may include criteria defined by specific density thresholds that distinguish the LA’s tissue (i.e., voxels representing LA in 3D grid) from surrounding structures (i.e., neighboring voxels). In some cases, such mask may further include a binary mask, wherein each voxel in the 3D grid may be assigned a first presence indicator such as 1 if the voxel meets the criteria for the LA and a second presence indicator such as 0 if it does not. In some embodiments, mask may be directly applied to 3D grid, selecting, or modifying voxels or cells, thereby enabling processor 110 to highlight, exclude, or otherwise manipulate specific parts of a heart within 3D grid. Processor 110 may then perform an element-wise multiplication between 3D grid and the mask. Continuing from the previous non-limiting example, voxels corresponding to the LA (wherein the mask value is 1) may retain their original values, while other voxels (where the mask value is 0) may be set to 0 or other specific value (i.e., excluded or masked out). Still referring to FIG.1, in some embodiments, 3D grid may include one or more spatial features extracted from plurality of ultrasonic images 104. As used in this disclosure, “spatial features” are specific characteristics or attributes related to the spatial arrangement, shape, size, texture, or orientation of structures within a 3D space. In some cases, spatial features may include one or more embedded values described herein and their combinations thereof. In a non- limiting example, spatial feature may be represented numerically as a vector, a metric or other mathematical constructs that capture specific spatial characteristics. In some cases, spatial features may also be visualized as contours, surfaces, or other geometric representations. In an embodiment, spatial features may be extracted using edge detection, texture analysis, or other image processing techniques (e.g., cleaning and enhancing images, image segmentation, and / or the like). In another embodiment, one or more machine learning models, such as convolutional neural networks (CNNs) as described in further detail below, may be used to extract complex spatial features. Still referring to FIG.1, in a non-limiting example, one or more spatial features may include one or more shape features (i.e., characteristics related to the shape of specific structures), such as curvature, surface area, volume, and / or the like. In another non-limiting 40 Attorney Docket No.1518-194PCT1 example, one or more spatial features may include one or more texture features (i.e., characteristics related to the texture or pattern within tissues, as seen in plurality of ultrasonic images 104), such as gray-level co-occurrence matrix (GLCM) features representing the texture of heart muscle tissue. In another non-limiting example, one or more spatial features may include one or more orientation features (i.e., characteristics related to the orientation or alignment of structures), such as the angle or alignment of the septum within the heart. In a further non- limiting example, one or more spatial features may include one or more edge and boundary features (i.e., Characteristics related to the edges or boundaries between different structures), such as edge detection features highlighting the boundary between the myocardium and the cardiac chambers. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various spatial features extracted from plurality of ultrasonic images 104 consistent with this disclosure. Still referring to FIG.1, system 100 may determine 3D cardiac model as a function of plurality of ultrasonic images 104. As used herein, a “3D cardiac model” is a 3D representation of a subject’s heart. In some embodiments, system 100 may determine 3D cardiac model using 3D cardiac model generation machine learning model.3D cardiac model generation machine learning model may be trained to interpret ultrasonic images and / or generate 3D cardiac models by learning relationships between ultrasonic images and corresponding computed tomography (CT) scan data.3D cardiac model generation machine learning model may be trained using a supervised learning algorithm.3D cardiac model generation machine learning model may include a neural network.3D cardiac model generation machine learning model may be trained on a training dataset including example ultrasonic images 104, associated with example 3D cardiac models. Example ultrasonic images 104 may be generated based on historical CT scan data 144. For example, a 3D model generated based on CT scan data may be used to determine what ultrasonic images would contain when taken from varying perspectives. Training dataset may be obtained by, for example, associating historical ultrasonic images with historical CT scan based 3D cardiac models. Once 3D cardiac model generation machine learning model is trained, it may be used to determine 3D cardiac model. System 100 may input plurality of ultrasonic images 104 into 3D cardiac model generation machine learning model, and system 100 may receive 3D cardiac model from 3D cardiac model generation machine learning model. Still referring to FIG.1, in some embodiments, a training dataset may be generated by 41 Attorney Docket No.1518-194PCT1 correlating an instance of computed tomography scan data with one or more historical ultrasonic images as a function of a medical record and a language model. For example, a language model may be used to interpret a medical record and / or determine whether an instance of computed tomography scan data should be associated with a historical ultrasonic image in a training dataset. For example, a language model may be used to interpret language of a medical record, and the output of the language model may be used to identify whether a medical event has taken place between when the historical ultrasonic image was taken and when the historical computed tomography scan data was recorded, such that they are not to be associated in a training dataset. In another example, a language model may be used to interpret language of a medical record, and the output of the language model may be used to identify whether historical ultrasonic image and historical computed tomography scan data were recorded in a sufficiently short time, such that they are associated in a training dataset. In some embodiments, a training dataset may be identified by generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data. Still referring to FIG, 1, in some embodiments, training dataset may include 3D cardiac models including computed tomography (CT) based 3D model. As used in this disclosure, a “computed tomography (CT) based 3D model” is a 3D representation of a structure that is created using data from CT scans. In some embodiments, a computed tomography (CT) based 3D model may include a 3D representation of a structure and surrounding structures that is created using data from CT scans. Computed Tomography is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of the body. By taking a plurality of slices, a CT scan creates a detailed 3D representation of the internal structure. In an embodiment, CT- based 3D model may include 3D representations of the heart including chambers, valves, blood vessels, and surrounding tissues. CT-based 3D models may be generated using existing techniques in the field as described above such as, without limitation, FAM, cardiac CT merging, among others. Still referring to FIG.1, processor 110 may be configured to receive at least an ultrasound localization datum. As used in this disclosure, an “ultrasound localization datum” is a unit of information that represents a position and / or angle of an ultrasound device. In some cases, ultrasound device may have incorporated one or more position sensors (e.g., accelerometers, gyroscope, inertial measurement units, magnetics location sensors, and the like) and / or be 42 Attorney Docket No.1518-194PCT1 incorporated with an localization system. In some cases, one or more of ultrasound device and localization system communicates at least an ultrasound localization datum to processor 110. Processor may be configured to generate, using 3D cardiac model generation machine learning model, the 3D cardiac model as a function of at least an ultrasound localization datum Still referring to FIG.1, processor 110 may be configured to generate a 3D voxel occupancy representation (VOR) representing a cardiac shape as a function of plurality of ultrasonic images and 3D cardiac model generation machine learning model trained. In some versions, processor 110 may generate a mesh representing cardiac shape as a function of 3D voxel occupancy representation. Processor 110 may be configured to display, using display, a mesh to a user. Still referring to FIG.1, processor 110 may be configured to calculate a level of uncertainty at a plurality of locations on 3D cardiac model. In some cases, plurality of locations may include a high uncertainty region. Processor 110 may be configured to receive a subsequent plurality of ultrasonic images of cardiac anatomy corresponding to a high uncertainty region of 3D cardiac model. In some cases, subsequent plurality of ultrasonic images may be captured using ultrasonic imaging device (e.g., ICE, TEE, TTE, and / or POCUS), as a function of high uncertainty region. Processor 110 may be configured to generate a subsequent 3D cardiac model as a function of subsequent plurality of ultrasonic images. With continued reference to FIG.1, in some cases, generating 3D cardiac model 126 and / or 3D catheter model 128 may include generating 3D cardiac model 126 and / or 3D catheter model 128 using a statistical shape model 134. As used in this disclosure, a “statistical shape model (SSM)” is a computer algorithm that generates a data structure representing, including, and / or utilizing a mathematical model that captures principal modes of variation in shape across a population of cardiac anatomies. SSM 134 captures a plurality of heart models associated with a plurality of patients. In some cases, SSM 134 may be used to capture the variability in anatomical structures among different patients; for instance, SSM 134 of the human heart may be constructed from a plurality of heart images of a plurality of individuals. In some cases, 3D cardiac model 126 and / or 3D catheter model 128 generated by SSM model may capture the “average” heart shape and main ways in which heart shapes may vary among the plurality of individuals. In one or more embodiments, 3D cardiac model 126 and / or 3D catheter model 128 generated by SSM model may capture the “average” of the plurality of anatomical objects in 43 Attorney Docket No.1518-194PCT1 which anatomical objects may vary among plurality of individuals. In some cases, SSM 134 may be generated by processor 110 as a function of a set of labeled example shapes, each in a form of point-based representations (3D point cloud 130) or meshes. In some cases, example shapes may be represented in a 3D voxel occupancy representation (VOR). With continued reference to FIG.1, generating 3D cardiac model may include generating 3D cardiac model using a point completion model. For the purposes of this disclosure, a “point completion model” is an algorithm that is configured to fill in missing data within a point cloud. In some embodiments, point completion model may include one or more deep learning models. In some embodiments, point completion model may include point-based methods. Point based methods may include modeling each point individually using multilayer perceptron (MLP) layers. In some embodiments, features may be learned from raw input point cloud data. This may reduce reliance on prior information and / or manually set parameters. In some embodiments, point-based methods may use an encoder-decoder architecture. In some embodiments, encoder- decoder may include an end-to-end cascaded neutral network. In some embodiments, point completion model may use a selective focus approach. A selective focus approach may include an attention mechanism. Attention is an adaptive mechanism that is used to capture information and assign higher weights to important data. In some embodiments, point completion model may include an unsupervised 3D point cloud capsule network that uses autoencoders to process sparse point clouds and preserve the spatial arrangement. In some embodiments, attention mechanisms may be used to enhance the resolution and fill in missing parts. In some embodiments, point completion model may use a view-guided approach. Relying on a single view may be susceptible to scene and temporal limitations, which can result in lost details. In some embodiments, a view- guided framework (ViPC) that retrieves absent global shape information from alternative single- view images may be used. By including additional single-view images, ViPC may provide global structural prior information for point cloud completion. In some embodiments, point completion model may use a point completion network (PCN)-assisted approach. Some deep-learning methods usually discretize 3D data into voxels that act directly on the convolution operations. Instead, in some embodiments, a multi-stage point cloud completion network (MSPCN) with crucial set oversight, incorporating a cascading upsampling module to achieve high-resolution outcomes gradually may be used. The vital sets for each stage may be used for oversight, thereby generating more informative and valuable intermediary outputs for the subsequent stage. In some 44 Attorney Docket No.1518-194PCT1 embodiments, a skeleton-bridged PCN (SK-PCN) to complement shapes by locally scanning them may be used. Initially the SK-PCN may forecast their 3D skeleton to attain a universal structure, and completing surfaces by learning the shifts in the skeleton points. In some embodiments a point enhancement network (ME-PCN) may use the “void” in the 3D shape space to approximate rough but complete and coherent surface points and then produce fine- grained surface details in the refinement stage. A local-to-local strategy may be used in some embodiments, and an attentional point cloud aggregation module may be used to aggregate local scans to complete point clouds. Without limitation, generating a three-dimensional (3D) model of anatomical object may include machine-learning. Without limitation, the system 100 may include receiving, by at least a processor, a set of images of an anatomical object pertaining to a subject. In some embodiments, receiving the set of images comprises receiving the set of images from a patient profile. Without limitation, the at least a processor may generate an 3D data structure representing the anatomical object as a function of the set of images. In some embodiments, the 3D data structure representing the anatomical object may include a 3D voxel occupancy representation (VOR) having plurality of voxels, wherein each voxel of the plurality of voxels may include a corresponding presence indicator. In other embodiments, the 3D data structure representing the anatomical object may include a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid may include one or more spatial features extracted from the set of images of the anatomical object. Without limitation, the at least a processor may generate 3D data structure further includes receiving anatomy training data, wherein the anatomy training data contains a plurality of image sets as input and a plurality of computed tomography (CT) based anatomical object models as output, training an anatomy modeling model using the anatomy training data, and generating the 3D data structure representing the anatomical object as a function of the set of images using the trained anatomy modeling model. In some embodiments, the anatomy modeling model may include a Deep Neural Network (DNN). Without limitation, the system 100 may include generating the 3D VOR by generating a set of shape parameters based on the set of images of the anatomical object, wherein generating the set of shape parameters may include training a shape identification model using geometry training data, wherein the geometry training data contains the plurality of image sets as input 45 Attorney Docket No.1518-194PCT1 correlated to a plurality of shape parameter sets as output and generating the set of shape parameters as a function of the set of ultrasonic images using the trained shape identification model. Without limitation, the system 100 may include generating, by the at least a processor, an initial 3D model of the anatomical object. Without limitation, the system 100 may include refining, by the at least a processor, the generated initial 3D model of the anatomical object as a function of the 3D data structure representing the anatomical object. In some embodiments, the initial 3D model of the anatomical object may include a template model selected from a plurality of pre-determined template models. In some embodiments, refining the initial 3D model of the anatomical object may include deforming the template model to match the generated 3D data structure representing the anatomical object. In other embodiments, refining the initial 3D model of the anatomical object may include adjusting the subsequent 3D model of the anatomical object as a function of a set of shape parameters. Without limitation, the system 100 may include generating, by the at least a processor, a subsequent 3D model of the anatomical object as a function of the refinement. Without limitation, the system 100 may include generating a three-dimensional (3D) model of an anatomical object via machine-learning is described. Without limitation, the system 100 may include receiving, by at least a processor, a set of images of an anatomical object pertaining to a subject. Without limitation, the system 100 may include generating, by the at least a processor, anatomy training data using a 3D anatomical model, wherein the anatomy training data includes a plurality of image sets as input and a plurality of anatomical object models as output. Without limitation, the system 100 may include training, by the at least a processor, an anatomical modeling model using the generated anatomy training data. Without limitation, the system 100 may include generating, by the at least a processor, a three-dimensional (3D) data structure representing the anatomical object using the trained anatomy modeling model. Without limitation, the system 100 may include refining, by the at least a processor, an initial 3D model as a function of the 3D data structure representing the anatomical object. Without limitation, in one or more embodiments, the set of images include one or more 46 Attorney Docket No.1518-194PCT1 ultrasonic images. In one or more embodiments, the anatomical object includes an organ. In one or more embodiments, receiving, by the at least a processor, the set of images includes receiving the set of images from a patient profile. In one or more embodiments, receiving, the set of images from the patient profile further includes receiving (ECG) data associated with the subject form the patient profile. In one or more embodiments, the anatomy training data further includes the plurality of image sets and a plurality of ECG data as inputs and the plurality of anatomical object models as outputs. In one or more embodiments, the trained anatomy modeling model includes a multimodal machine learning model. In one or more embodiments, receiving, by the at least a processor, the set of images includes receiving the set of images from a patient profile. In one or more embodiments, generating, by the at least a processor, the anatomy training data using the 3D anatomical model includes classifying the set of images to an anatomical categorization and generating the anatomy training data using the 3D anatomical model as a function of the anatomical categorization. In one or more embodiments, the 3D anatomical model is configured to receive ongoing feedback and corrections to the 3D anatomical model and provide corrections to subsequent synthetic images. In one or more embodiments, generating the initial 3D model further includes generating a map visualizing a level of uncertainty on the 3D model. In one or more embodiments, the initial 3D model of the anatomical object includes a template model selected from a plurality of pre-determined template models. Without limitation, the system 100 may include generating a three-dimensional (3D) model of cardiac anatomy via machine-learning. Without limitation, the system 100 may include receiving, by at least a processor, a set of images of a cardiac anatomy pertaining to a subject. In some embodiments, receiving the set of images comprises receiving the set of images from a patient profile. Without limitation, the system 100 may include generating, by the at least a processor, an 3D data structure representing the cardiac anatomy as a function of the set of images. In some embodiments, the 3D data structure representing the cardiac anatomy may include a 3D voxel occupancy representation (VOR) having plurality of voxels, wherein each voxel of the plurality of voxels may include a corresponding presence indicator. In other embodiments, the 3D data structure representing the cardiac anatomy may include a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid may include one or more spatial features extracted from the set of images of the cardiac anatomy. 47 Attorney Docket No.1518-194PCT1 Without limitation, the system 100 may include generating the 3D data structure by receiving cardiac anatomy training data, wherein the cardiac anatomy training data contains a plurality of image sets as input and a plurality of computed tomography (CT) based cardiac anatomy models as output, training a cardiac anatomy modeling model using the cardiac anatomy training data, and generating the 3D data structure representing the cardiac anatomy as a function of the set of images using the trained cardiac anatomy modeling model. In some embodiments, the cardiac anatomy modeling model may include a Deep Neural Network (DNN). Without limitation, the system 100 may include generating the 3D VOR by generating a set of shape parameters based on the set of images of the cardiac anatomy, wherein generating the set of shape parameters may include training a shape identification model using cardiac geometry training data, wherein the cardiac geometry training data contains the plurality of image sets as input correlated to a plurality of shape parameter sets as output and generating the set of shape parameters as a function of the set of ICE images using the trained shape identification model. Without limitation, the system 100 may include generating, by the at least a processor, an initial 3D model of the cardiac anatomy. Without limitation, the system 100 may include refining, by the at least a processor, the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy. In some embodiments, the initial 3D model of the cardiac anatomy may include a template model selected from a plurality of pre-determined template models. In some embodiments, refining the initial 3D model of the cardiac anatomy may include deforming the template model to match the generated 3D data structure representing the cardiac anatomy. In other embodiments, refining the initial 3D model of the cardiac anatomy may include adjusting the subsequent 3D model of the cardiac anatomy as a function of a set of shape parameters. Without limitation, the system 100 may include generating, by the at least a processor, a subsequent 3D model of the cardiac anatomy as a function of the refinement. Without limitation, the system 100 may include generating a three-dimensional (3D) model of cardiac anatomy via machine-learning is illustrated. Without limitation, the system 100 may include receiving, by at least a processor, a set of images of a cardiac anatomy pertaining to a subject. In some embodiments, receiving the set of images comprises receiving the set of 48 Attorney Docket No.1518-194PCT1 images from a patient profile. Without limitation, the system 100 may include generating, by the at least a processor, an 3D data structure representing the cardiac anatomy as a function of the set of images. In some embodiments, the 3D data structure representing the cardiac anatomy may include a 3D voxel occupancy representation (VOR) having plurality of voxels, wherein each voxel of the plurality of voxels may include a corresponding presence indicator. In other embodiments, the 3D data structure representing the cardiac anatomy may include a 3D grid configured to map the presence indicators of the plurality of voxels, wherein the 3D grid may include one or more spatial features extracted from the set of images of the cardiac anatomy. Without limitation, the system 100 may include generating the 3D data structure by receiving cardiac anatomy training data, wherein the cardiac anatomy training data contains a plurality of image sets as input and a plurality of computed tomography (CT) based cardiac anatomy models as output, training a cardiac anatomy modeling model using the cardiac anatomy training data, and generating the 3D data structure representing the cardiac anatomy as a function of the set of images using the trained cardiac anatomy modeling model. In some embodiments, the cardiac anatomy modeling model may include a Deep Neural Network (DNN). Without limitation, the system 100 may include generating the 3D VOR by generating a set of shape parameters based on the set of images of the cardiac anatomy, wherein generating the set of shape parameters may include training a shape identification model using cardiac geometry training data, wherein the cardiac geometry training data contains the plurality of image sets as input correlated to a plurality of shape parameter sets as output and generating the set of shape parameters as a function of the set of ICE images using the trained shape identification model. Without limitation, the system 100 may include generating, by the at least a processor, an initial 3D model of the cardiac anatomy. Without limitation, the system 100 may include refining, by the at least a processor, the generated initial 3D model of the cardiac anatomy as a function of the 3D data structure representing the cardiac anatomy. In some embodiments, the initial 3D model of the cardiac anatomy may include a template model selected from a plurality of pre-determined template models. In some embodiments, refining the initial 3D model of the cardiac anatomy may include deforming the template model to match the generated 3D data structure representing the cardiac 49 Attorney Docket No.1518-194PCT1 anatomy. In other embodiments, refining the initial 3D model of the cardiac anatomy may include adjusting the subsequent 3D model of the cardiac anatomy as a function of a set of shape parameters. Without limitation, the system 100 may include generating, by the at least a processor, a subsequent 3D model of the cardiac anatomy as a function of the refinement. Without limitation, the system 100 may include synthetizing medical images. Without limitation, the system 100 may include receiving, by at least a processor, a heart model related to a patient's heart. In some embodiments, receiving the heart model may include constructing the heart model based on a patient profile pertaining to the patient using a computer vision module, wherein the patient profile may include a plurality of computed tomography (CT) scans of the patient's heart and associated metadata. In some cases, the patient profile further may include electrocardiogram (ECG) data. In some embodiments, receiving the heart model may include transforming the heart model to a second heart model using a statistical shape model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the heart model. In other cases, the heart model may include a 3D voxel occupancy representation (VOR) of the patient's heart. Without limitation, the system 100 may include identifying, by the at least a processor, a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model. Without limitation, the system 100 may include generating, by the at least a processor, at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model. In some embodiments, generating the at least a medical image may include executing a camera transformation program configured to simulate at least a perspective of an ICE probe, ultrasound probe, or other probe using the image generator. In some cases, executing the camera transformation program may include generating a projection of the anatomical structure by rendering the ROI as a function of a set of imaging parameters using a virtual camera positioned at the at least a point of view with the corresponding view angle. In some cases, the image 50 Attorney Docket No.1518-194PCT1 generator may include a generative adversarial network (GAN). In some embodiments, generating the at least a medical image may include training the GAN using a plurality of anatomical structure projections and synthesizing at least a medical image using the trained GAN at the at least a point of view with the corresponding view angle. Without limitation, the system 100 may include compiling, by the at least a processor, a plurality of medical images into a video as a function of the ECG data, wherein the video is synchronized with a cardiac cycle indicated by the ECG data. Without limitation, the system 100 may include synthetizing medical images. Without limitation, the system 100 may include receiving, by at least a processor, an ultrasound image of a patient's organ. In some embodiments, the ultrasound image of the patient's organ may include a transesophageal echocardiogram image. In some embodiments, the ultrasound image of the patient's organ may include a transthoracic echocardiogram image. In some embodiments, the ultrasound image of the patient's organ may include a point-of-care ultrasound image. Without limitation, the system 100 may include generating, by at least a processor, an organ model related to the patient's organ as a function of the ultrasound image. In some embodiments, generating the organ model includes transforming the organ model to a second organ model using a Statistical Shape Model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the organ model. In some embodiments, the organ model may include a heart model. In some embodiments, the heart model may include a model feature, wherein the model feature includes a thickness of a heart wall. Without limitation, the system 100 may include identifying, by the at least a processor, a region of interest within the organ model, wherein identifying the region of interest comprises: locating at least a point of view on the organ model and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the organ model. In some embodiments, identifying the region of interest within the organ model may include selecting a first set of points from a medical image. In some embodiments, identifying the region of interest within the organ model may include determining a second set of points on the organ model corresponding to the first set of points. In some embodiments, identifying the region of interest 51 Attorney Docket No.1518-194PCT1 within the organ model may include mapping a plurality of points of the medical image to the organ model using a relationship between the first set of points and the second set of points. In some embodiments, mapping the plurality of points of the medical image to the organ model using the relationship between the first set of points and the second set of points may include determining a rigid transformation from the first set of points to the second set of points. Without limitation, the system 100 may include generating, by the at least a processor, at least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the organ model. In some embodiments, generating the at least a medical image may include generating a plurality of medical images. Without limitation, the system 100 may include compiling, by the at least a processor, the plurality of medical images into a video. Without limitation, the system 100 may include displaying, by the at least a processor, the video on a display device. Without limitation, the system 100 may include synthetizing medical images. Without limitation, the system 100 may include receiving, by at least a processor, a heart model related to a patient's heart. In some embodiments, receiving the heart model may include constructing the heart model based on a patient profile pertaining to the patient using a computer vision module, wherein the patient profile may include a plurality of computed tomography (CT) scans of the patient's heart and associated metadata. In some cases, the patient profile further may include electrocardiogram (ECG) data. In some embodiments, receiving the heart model may include transforming the heart model to a second heart model using a statistical shape model as a function of a plurality of mode changers within the Statistical Shape Model, wherein each mode changer of the plurality of mode changers is associated with a model feature of the heart model. In other cases, the heart model may include a 3D voxel occupancy representation (VOR) of the patient's heart. Without limitation, the system 100 may include identifying, by the at least a processor, a region of interest within the heart model, wherein identifying the region of interest includes locating at least a point of view on the heart model and determining a view angle corresponding to the at least a point of view, wherein the at least a point of view and the corresponding view angle define at least one field of view that include at least a portion of the heart model. Without limitation, the system 100 may include generating, by the at least a processor, at 52 Attorney Docket No.1518-194PCT1 least a medical image as a function of the region of interest using an image generator, wherein the at least a medical image captures an anatomical structure of the at least a portion of the heart model. In some embodiments, generating the at least a medical image may include executing a camera transformation program configured to simulate at least a perspective of an ICE probe or other probe using the image generator. In some cases, executing the camera transformation program may include generating a projection of the anatomical structure by rendering the ROI as a function of a set of imaging parameters using a virtual camera positioned at the at least a point of view with the corresponding view angle. In some cases, the image generator may include a generative adversarial network (GAN). In some embodiments, generating the at least a medical image may include training the GAN using a plurality of anatomical structure projections and synthesizing at least a medical image using the trained GAN at the at least a point of view with the corresponding view angle. Without limitation, the system 100 may further include a step of compiling, by the at least a processor, a plurality of medical images into a video as a function of the ECG data, wherein the video is synchronized with a cardiac cycle indicated by the ECG data. Without limitation, generating a three-dimensional (3D) model of patient's organ may include cardiac image capture device. Cardiac image capture device may include a cardiac image capture device. For example, cardiac image capture device may include an ICE catheter. Without limitation, the system may further include computing device. Computing device may receive a first set of images, such as a first set of ICE images, from cardiac image capture device. Computing device may perform one or more processing steps, such as application of a shape identification model to generate a set of shape parameters, application of a statistical shape model to generate a 3D model, determination of a level of uncertainty and / or a map, overlay of a map onto a 3D model, and / or display of a map and / or a 3D model to a user using user interface and / or user device. User may receive information, such as information as to a level of uncertainty at a particular location of a 3D model and / or patient's organ and may position cardiac image capture device within patient's organ in order to capture a second set of images, such as a second set of ICE images. User may perform this through, for example, interaction with user interface. Second set of images may be used to generate an updated 3D model, an updated map, and / or an updated level of uncertainty, which may be displayed to user through user interface. Without limitation, in some cases, TEE may be performed during another procedure for 53 Attorney Docket No.1518-194PCT1 instance heart surgery. According to some embodiments, a patient has an endoscope, with an ultrasonic transducer, inserted into his esophagus. As one's esophagus is proximal one's heart, ultrasonic transducer may generate echocardiograms. Without limitation, the system 100 may generate a three-dimensional (3D) model of cardiac anatomy. System 100 may include cardiac image capture device. Cardiac image capture device may include a cardiac image capture device described with reference to another figure herein. For example, cardiac image capture device may include an ICE catheter. System may further include computing device. Computing device may receive a first set of images, such as a first set of ICE images, from cardiac image capture device. Computing device may perform one or more processing steps described herein, such as application of a shape identification model to generate a set of shape parameters, application of a statistical shape model to generate a 3D model, determination of a level of uncertainty and / or a map, overlay of a map onto a 3D model, and / or display of a map and / or a 3D model to a user using user interface and / or user device. User may receive information, such as information as to a level of uncertainty at a particular location of a 3D model and / or cardiac anatomy and may position cardiac image capture device within cardiac anatomy in order to capture a second set of images, such as a second set of ICE images. User may perform this through, for example, interaction with user interface 172. Second set of images may be used to generate an updated 3D model, an updated map, and / or an updated level of uncertainty, which may be displayed to user through user interface 172. Without limitation, the system 100 may further include generating a three-dimensional (3D) model of cardiac anatomy with an overlay is illustrated. Without limitation, the system 100 may further include receiving, by a processor, a set of images of a cardiac anatomy pertaining to a subject. Without limitation, the system 100 may further include generating, by the processor, a set of shape parameters based on the set of images, wherein generating the set of shape parameters comprises receiving cardiac geometry training data comprising a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model. Without limitation, the system 100 may further include generating, by the processor, a 3D model of the cardiac anatomy based on the set of shape parameters. Without limitation, the system 100 may further include generating, by the processor, a map by determining a level of uncertainty at each location of a plurality of locations 54 Attorney Docket No.1518-194PCT1 on the generated 3D model. Without limitation, the system 100 may further include overlaying, by the processor, the 3D model with the map. Without limitation, the system 100 may further include generating a three-dimensional (3D) model of cardiac anatomy. Without limitation, the system 100 may further include receiving a first set of images of cardiac anatomy. In some embodiments, capturing a second set of images may include using a display device, displaying the first 3D model of the cardiac anatomy to a user; and by the user, positioning the cardiac image capture device for capturing an image of the low confidence region. In some embodiments, displaying the first 3D model of the cardiac anatomy to the user may include generating a first map by determining a level of uncertainty at each location of a plurality of locations on the generated first 3D model; and overlaying the first map onto the first 3D model. In some embodiments, the first map identifies the high uncertainty region of the first 3D model. In some embodiments, the first map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the first 3D model. In some embodiments, the cardiac image capture device comprises an intracardiac echocardiography catheter. Without limitation, the system 100 may further include generating a first 3D model of the cardiac anatomy as a function of the first set of images. In some embodiments, generating the first 3D model includes generating a set of shape parameters based on the first set of images; generating the set of shape parameters includes receiving cardiac geometry training data comprising a plurality of image sets as inputs correlated to a plurality of shape parameter sets as outputs; training a shape identification model using the cardiac geometry training data; and generating the set of shape parameters using the shape identification model; and the first 3D model is generated based on the set of shape parameters. In some embodiments, the high uncertainty region is determined using model output uncertainty. In some embodiments, the plurality of shape parameter sets of the cardiac geometry training data is generated using computed tomography. In some embodiments, the shape identification model includes a deep neural network. In some embodiments, the shape identification model includes a convolutional neural network. In some embodiments, the second 3D model is generated using the shape identification model and the second set of images. In some embodiments, the second 3D model is generated using the first set of images. In some embodiments, generating the first 3D model further comprises using a statistical shape model to generate the first 3D model as a function of 55 Attorney Docket No.1518-194PCT1 the set of shape parameters. In some embodiments, the set of shape parameters includes a plurality of numerical descriptors, wherein each numerical descriptor of the plurality of numerical descriptors represents a geometric characteristic of the cardiac anatomy. In some embodiments, each shape parameter within the set of shape parameters includes a corresponding parameter range. Without limitation, the system 100 may further include calculating a level of uncertainty at each location of a plurality of locations on the first 3D model. Without limitation, the system 100 may further include receiving a second set of images of the cardiac anatomy corresponding to a high uncertainty region of the first 3D model. Without limitation, the system 100 may further include generating a second 3D model as a function of the second set of images. Without limitation, the system 100 may further include displaying the second 3D model to a user. In some embodiments, displaying the second 3D model of the cardiac anatomy to the user includes generating a second map by determining a level of uncertainty at each location of a plurality of locations on the generated second 3D model; and overlaying the second map onto the second 3D model. In some embodiments, the second map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the second 3D model. Without limitation, in some embodiments, the at least a processor may use a plurality of cardiac image capture devices. For example, a first set of images may be captured using a first cardiac image capture device and a second set of images may be captured using a second cardiac image capture device. Without limitation, the system 100 may further include generating a three-dimensional (3D) model with an overlay is illustrated. Without limitation, the system 100 may further include receiving a set of ultrasonic images of an organ of a subject. In some embodiments, receiving the set of ultrasonic images comprises receiving the set of ultrasonic images from a patient profile. In some embodiments, the organ is a heart. In some embodiments, a set of ultrasonic images of the patient's organ may include an image selected from the list consisting of a transesophageal echocardiogram image, a transthoracic echocardiogram image, and a point-of-care ultrasound image. Without limitation, the system 100 may further include generating a set of shape parameters representing the organ's shape as a function of the set of ultrasonic images and a 56 Attorney Docket No.1518-194PCT1 shape identification model trained on a training dataset comprising historical ultrasonic images correlated with historical computed tomography scan data. In some embodiments, the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of the organ. In some embodiments, each shape parameter within the set of shape parameters is associated with a corresponding parameter range. Without limitation, the system 100 may further include generating a 3D model of the organ based on the set of shape parameters. In some embodiments, generating the 3D model further includes generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained. Without limitation, the system 100 may further include generating a map by determining a level of uncertainty at each location of a plurality of locations on the 3D model. In some embodiments, the map includes a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model. Without limitation, the system 100 may further include overlaying the map onto the 3D model. Without limitation, the system 100 may further include identifying the training dataset and / or training the shape identification model on the training dataset. In some embodiments, identifying a training dataset may include correlating an instance of computed tomography scan data with a historical ultrasonic image as a function of a medical record and a language model. In some embodiments, identifying a training dataset may include generating a synthetic ultrasonic image as a function of an instance of computed tomography scan data. Without limitation, the system 100 may further include determining a Left Atrial Appendage Occlusion Device placement as a function of the 3D model. Without limitation, the system 100 may further include generating a three-dimensional (3d) model of cardiac anatomy with an overlay. Without limitation, the system 100 may further include receiving, by a processor, a set of images of a cardiac anatomy pertaining to a subject. Without limitation, the system 100 may further include generating, by the processor, a set of shape parameters based on the set of images, wherein generating the set of shape parameters comprises receiving cardiac geometry training data comprising a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of chape parameters using the 57 Attorney Docket No.1518-194PCT1 shape identification model. Without limitation, the system 100 may further include generating, by the processor, a 3D model of the cardiac anatomy based on the set of shape parameters. Without limitation, the system 100 may further include generating, by the processor, a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model. Without limitation, the system 100 may further include overlaying, by the processor, the 3D model with the map. Without limitation, in an embodiment of the system 100 may further provide visualization within the model. Without limitation, the system 100 may further include receiving, by at least a processor, a query image. In one or more embodiments, query image may be a query medical image, such as a query ICE frame. Without limitation, the system 100 may further include extracting, by at least a processor, neural network encodings as a function of the received query image. In one or more embodiments, neural network encodings may be extracted by generating plurality of shape parameters, wherein the generation of shape parameters may involve training a pattern recognition model. Without limitation, the system 100 may further include querying, by at least a processor, a synthetic image repository for at least a matching synthetic image based on extracted neural network encodings of query image. In one or more embodiments, synthetic image repository may contain plurality of synthetic images and their corresponding neural network encodings. In one or more embodiments, plurality of synthetic images may be generated by executing camera transformation program configured to simulate at least a perspective of image capture device. In one or more embodiments, plurality of synthetic medical images may be generated using an image translation model. Without limitation, the system 100 may further include displaying, by at least a processor, estimated ROI of query image within 3D model by positioning query image as a function of at least a matching synthetic image. In one or more embodiments, displaying estimated ROI within the 3D model may include overlaying 2D cross section containing the estimated ROI within at least a portion of the 3D model. With continued reference to FIG.1, memory 114 contains instructions configuring processor 110 to receive at least a 3D stent model 136 representative of a stent. For the purposes of this disclosure, a “three-dimensional stent model” is a three-dimensional visual representation 58 Attorney Docket No.1518-194PCT1 of a stent. In some embodiments, 3D stent model 136 may be consistent with 3D cardiac model 126 and / or 3D catheter model 128. In some embodiments, processor 110 may retrieve 3D stent model 136 from a stent database 116. In some embodiments, user may manually input 3D stent model 136 into computing device 112. In some embodiments, processor 110 may receive ultrasound image 104 from TEE system 102, wherein the ultrasound image 104 may include an image of a catheter with stent entering a coronary artery of a heart or stent getting placed within coronary artery, and processor 110 may extract an image of stent and generate 3D stent model 136. With continued reference to FIG.1, in some embodiments, receiving at least a 3D stent model 136 may include extracting at least an artery featuring datum 138 from at least an ultrasound image 104, determining a stent datum 140 as a function of the at least an artery featuring datum 138 and patient data 142, and generating the at least a 3D stent model 136 as a function of the stent datum 140. For the purposes of this disclosure, an “artery featuring datum” is a data element indicating a value representing one or more properties of an artery of a heart for a stent placement. In some embodiments, cardiac featuring datum 120 may include artery featuring datum 138. As a non-limiting example, artery featuring datum 138 may include diameter, shape, depth, thickness, orientation, or geometry of a coronary of a heart. In some embodiments, artery featuring datum 138 may be stored in a database. In some embodiments, artery featuring datum 138 may be retrieved from database. In some embodiments, user may manually input artery featuring datum 138. In some embodiments, processor 110 may determine artery featuring datum 138 using machine vision system, image processing techniques, and the like. For the purposes of this disclosure, a “stent datum” is a data element that indicates a value or set of values describing properties of a stent. In some embodiments, stent datum 140 may include a size datum 144. For the purposes of this disclosure, a “size datum” is a data element that indicates a size of a stent. As a non-limiting example, stent datum 140 may include parameters such as a particular stent’s nominal size, expanded diameter, its shape or configuration, anchoring features, and the like. In a non-limiting example, determining stent datum 140 may include determining which stent of a plurality of stents will best match the anatomy of a patient’s heart (artery featuring datum 138). In some embodiments, processor 110 may generate 3D stent model 136 that reflects stent that is determined by processor 110. With continued reference to FIG.1, for the purposes of this disclosure, “patient data” is 59 Attorney Docket No.1518-194PCT1 any information related to a patient. As a non-limiting example, patient data 142 may include age, gender, name, medical and / or surgical history, pre-existing condition, physiological information, diagnostic information, and the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, may appreciate various user information that may be used as patient data 142. In some embodiments, patient data 142 may be retrieved from a database. In some embodiments, user may manually input patient data 142. With continued reference to FIG.1, in some embodiments, processor 110 may be configured to generate stent training data 146. In a non-limiting example, stent training data 146 may include correlations between exemplary artery featuring data, exemplary patient data and exemplary stent data. In some embodiments, stent training data 146 may be stored in stent database 116. In some embodiments, stent training data 146 may be received from one or more users, stent database 116, external computing devices, and / or previous iterations of processing. As a non-limiting example, stent training data 146 may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, which may be stored in memory and / or stored in stent database 116, where the instructions may include labeling of training examples. In some embodiments, stent training data 146 may be updated iteratively on a feedback loop. As a non-limiting example, processor 110 may update stent training data 146 iteratively through a feedback loop as a function of artery featuring datum 138, stent datum 140, ultrasound image 104, or the like. In some embodiments, processor 110 may be configured to generate a stent machine-learning model 148. In a non-limiting example, generating stent machine-learning model 148 may include training, retraining, or fine-tuning stent machine-learning model 148 using stent training data 146 or updated stent training data 146. In some embodiments, processor 110 may be configured to determine stent datum 140 using stent machine-learning model 148 (i.e., trained or updated stent machine-learning model 148). In some embodiments, patient, patient’s heart or ultrasound image 104 may be classified to a patient cohort using a cohort classifier. Cohort classifier may be consistent with any classifier discussed in this disclosure. Cohort classifier may be trained on cohort training data, wherein the cohort training data may include patient, patient’s heart or ultrasound image 104 correlated to patient cohorts. In some embodiments, patient, patient’s heart or ultrasound image 104 may be classified to a patient cohort and processor 110 may determine stent datum 140 based on the patient cohort using a machine-learning module as described in detail with respect to FIG.6 and 60 Attorney Docket No.1518-194PCT1 the resulting output may be used to update stent training data 146. In some embodiments, generating training data and training machine-learning models may be simultaneous. With continued reference to FIG.1, in some embodiments, determining stent datum 140 may include simulating a placement of a plurality of stents within at least a 3D cardiac model 126 and / or 3D catheter model 128 as a function of at least an artery featuring datum 138 and determining the stent datum 140 as a function of the simulation. In a non-limiting example, processor 110 may use finite element analysis (FEA) or similar computational methods to simulate how stents interacts with a coronary artery of a patient’s heart. In some embodiments, determining stent datum 140 may include determining a placement datum 150 as a function of at least an artery featuring datum 138. In some embodiments, generate placement datum 150 for simulation of a placement of a plurality of stents within at least a 3D cardiac model 126 and / or 3D catheter model 128. For the purposes of this disclosure, a “placement datum” is a data element that indicates a value representing whether a stent is deemed suitable for placement within a coronary artery. As a non-limiting example, placement datum 150 may include a position datum that represents spatial orientation and location of stent implanted within coronary artery during simulation or placement process. As another non-limiting example, placement datum 150 may include an anchor datum that describes the ability of stent to anchor securely within coronary artery. As another non-limiting example, placement datum 150 may include a placement size datum that indicates the dimensions of stent, such as its expanded diameter, length, or coverage area, ensuring compatibility with coronary artery. As another non-limiting example, placement datum 150 may include a seal datum that refers to stent’s capacity to effectively open a clogged or narrowed arteries. With continued reference to FIG.1, memory 114 contains instructions configuring processor 110 to determine a view label 152 for each of a plurality of ultrasound images 104. For the purposes of this disclosure, a “view label” is an identifier associated with an imaging perspective or orientation of a heart captured during a transesophageal echocardiography. As a non-limiting example, view label 152 may include mid-esophageal four-chamber view, mid- esophageal bicaval view, mid-esophageal long-axis view, mid-esophageal left atrial appendage view, transgastric short-axis view, transgastric two-chamber view, aortic valve short-axis view, and the like. In some embodiments, view label 152 may be retrieved from stent database 116. In some embodiments, user may manually input view label 152 of ultrasound images 104. 61 Attorney Docket No.1518-194PCT1 With continued reference to FIG.1, in some embodiments, view training data 154 may be stored in stent database 116. For the purposes of this disclosure, “view training data” is data containing correlations that a machine-learning process may use to model relationships between ultrasound images and view labels. In some embodiments, view training data 154 may be received from one or more users, stent database 116, external computing devices, and / or previous iterations of processing. As a non-limiting example, view training data 154 may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, which may be stored in memory and / or stored in Stent database 116, where the instructions may include labeling of training examples. In some embodiments, view training data 154 may be updated iteratively on a feedback loop. As a non-limiting example, processor 110 may update view training data 154 iteratively through a feedback loop as a function of ultrasound image 104, or the like. In some embodiments, processor 110 may be configured to generate a view classifier 156. For the purposes of this disclosure, a “view classifier” is a machine-learning model as defined below, such as a data structure representing and / or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts ultrasound images into view labels. In a non-limiting example, generating view classifier 156 may include training, retraining, or fine-tuning view classifier 156 using view training data 154 or updated view training data 154. In some embodiments, processor 110 may be configured to determine view label 152 using view classifier 156 (i.e., trained or updated view classifier 156). In some embodiments, generating training data and training machine-learning models may be simultaneous. With continued reference to FIG.1, in some embodiments, determining a view label 152 may include extracting at least a TEE angle datum 158 from at least an ultrasound image 104 using an optical character recognition 160. For the purposes of this disclosure, a “transesophageal echocardiogram angle datum” is a data element indicating a value that represents the angular orientation of an ultrasound sensor. In some embodiments, TEE angle datum 158 may be angular orientation of an ultrasound sensor 106 relative to a reference axis or plane (e.g., the anatomical position of a heart). As a non-limiting example, TEE angle datum 158 may include TEE probe's imaging angle, such as 0°, 45°, 90°, or 135°, which may determine the plane of the ultrasound slice captured during imaging. 62 Attorney Docket No.1518-194PCT1 With continued reference to FIG.1, in some embodiments, processor 110 may analyze ultrasound image 104 to find TEE angle datum 158 using optical character recognition (OCR) 160. In some embodiments, ultrasound image 104 may include a plurality of words related to position and orientation of ultrasound sensor 106 within esophagus. For the purposes of this disclosure, “optical character recognition” is a technology that enables the recognition and conversion of printed or written text into machine-encoded text. In some cases, the at least a processor 110 may be configured to recognize a keyword using the OCR 160 to find the TEE angle datum 158. As used in this disclosure, a “keyword” is an element of word or syntax used to identify and / or match elements to each other. In some cases, the at least a processor 110 may transcribe much or even substantially all ultrasound images 104. With continued reference to FIG.1, in some embodiments, optical character recognition 160 or optical character reader (OCR) may include automatic conversion of images of written (e.g., typed, handwritten or printed text) into machine-encoded text. In some cases, recognition of a keyword from ultrasound image 104 may include one or more processes, including without limitation optical character recognition (OCR) 160, optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR 160 may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes. With continued reference to FIG.1, in some cases, OCR 160 may be an “offline” process, which analyses a static document or image frame. In some cases, handwriting movement analysis can be used as input to handwriting recognition. For example, instead of merely using shapes of glyphs and words, this technique may capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information may make handwriting recognition more accurate. In some cases, this technology may be referred to as “online” character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition. With continued reference to FIG.1, in some cases, OCR processes may employ pre- 63 Attorney Docket No.1518-194PCT1 processing of ultrasound image 104. Pre-processing process may include without limitation de- skew, de-speckle, binarization, line removal, layout analysis or “zoning,” line and word detection, script recognition, character isolation or “segmentation,” and normalization. In some cases, a de-skew process may include applying a transform (e.g., homography or affine transform) to the ultrasound image 104 to align text. In some cases, a de-speckle process may include removing positive and negative spots and / or smoothing edges. In some cases, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image). Binarization may be performed as a simple way of separating text (or any other desired image component) from a background of image component. In some cases, binarization may be required for example if an employed OCR algorithm only works on binary images. In some cases, a line removal process may include removal of non-glyph or non- character imagery (e.g., boxes and lines). In some cases, a layout analysis or “zoning” process may identify columns, paragraphs, captions, and the like as distinct blocks. In some cases, a line and word detection process may establish a baseline for word and character shapes and separate words, if necessary. In some cases, a script recognition process may, for example in multilingual documents, identify script allowing an appropriate OCR algorithm to be selected. In some cases, a character isolation or “segmentation” process may separate signal characters, for example character-based OCR algorithms. In some cases, a normalization process may normalize aspect ratio and / or scale of image component. With continued reference to FIG.1, in some embodiments an OCR process may include an OCR algorithm. Exemplary OCR algorithms include matrix matching process and / or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by-pixel basis. In some case, matrix matching may also be known as “pattern matching,” “pattern recognition,” and / or “image correlation.” Matrix matching may rely on an input glyph being correctly isolated from the rest of the image component. Matrix matching may also rely on a stored glyph being in a similar font and at a same scale as input glyph. Matrix matching may work best with typewritten text. With continued reference to FIG.1, in some embodiments, an OCR process may include a feature extraction process. In some cases, feature extraction may decompose a glyph into a feature. Exemplary non-limiting features may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In some cases, feature extraction may reduce 64 Attorney Docket No.1518-194PCT1 dimensionality of representation and may make the recognition process computationally more efficient. In some cases, extracted feature may be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR 160. In some embodiments, machine-learning processes like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) may be used to compare image features with stored glyph features and choose a nearest match. OCR 160 may employ any machine-learning process described in this disclosure, for example machine-learning processes described with reference to FIG.7. Exemplary non-limiting OCR software may include Cuneiform and Tesseract. Cuneiform may include a multi-language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract may include free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States. With continued reference to FIG.1, in some cases, OCR 160 may employ a two-pass approach to character recognition. A first pass may try to recognize a character. Each character that is satisfactory may be passed to an adaptive classifier as training data. The adaptive classifier then may get a chance to recognize characters more accurately as it further analyzes ultrasound image 104. Since the adaptive classifier may have learned something useful a little too late to recognize characters on the first pass, a second pass may be run over the ultrasound image 104. Second pass may include adaptive recognition and use characters recognized with high confidence on the first pass to recognize better remaining characters on the second pass. In some cases, two-pass approach may be advantageous for unusual fonts or low-quality image components where visual verbal content may be distorted. Another exemplary OCR software tool may include OCRopus. OCRopus development is led by German Research Centre for Artificial Intelligence in Kaiserslautern, Germany. In some cases, OCR software may employ neural networks. With continued reference to FIG.1, in some cases, OCR 160 may include post- processing. For example, OCR accuracy may be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters. In some cases, an OCR process may preserve an original 65 Attorney Docket No.1518-194PCT1 layout of visual verbal content. In some cases, near-neighbor analysis can make use of co- occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In some cases, an OCR process may make us of a priori knowledge of grammar for a language being recognized. For example, grammar rules may be used to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post- processing to further optimize results. With continued reference to FIG.1, in some embodiments, processor 110 may generate an image inquiry datum 162 as a function of the at least a TEE angle datum 158 and instruction for use (IFU) data 164 retrieved from an stent database 116. For the purposes of this disclosure, an “image inquiry datum” is a data element that inquires additional ultrasound images. In some embodiments, image inquiry datum 162 may include a control signal that is transmitted to TEE system 102 to obtain necessary additional ultrasound images 104. In a non-limiting example, if processor 110 determines that ultrasound images in specific views or specific angles and / or a number of ultrasound images are missing based on IFU data 164, processor 110 may request additional ultrasound images 104 from TEE system 102. In some embodiments, image inquiry datum 162 may include a notification for a user. In a non-limiting example, if processor 110 determines that ultrasound images in specific views or specific angles and / or a number of ultrasound images are missing based on IFU data 164, processor 110 may generate a notification and may transmit to a user to operate TEE system 102 to generate additional ultrasound images 104 more and / or in the specific views or specific angles. With continued reference to FIG.1, for the purposes of this disclosure, “Instructions for Use (IFU) data” is data related to procedural guidelines or specifications associated with a stent. As a non-limiting example, IFU data 164 may include usage instructions, imaging requirements, and the like. For example, and without limitation, IFU data 164 may include specific views or angles of ultrasound images 104 necessary for a placement of stent. For example, and without limitation, IFU data 164 may include a number of ultrasound images 104 necessary for a placement of stent. In some embodiments, IFU data 164 may be stored in a stent database 116. In some embodiments, processor 110 may retrieve IFU data 164 from stent database 116 as a function of stent datum 140. As a non-limiting example, processor may retrieve IFU data 164 66 Attorney Docket No.1518-194PCT1 that is related to a stent selected specifically for a patient. With continued reference to FIG.1, memory 114 contains instructions configuring processor 110 to display, using at least a display 108, at least a 3D cardiac model 126 and at least a 3D stent model 136 as a function of a view label 152. For the purposes of this disclosure, a “display” is a device that presents visual information or data. As a non-limiting example, display 108 may present visual information or data in one or more forms of text, graphics, images, video, animation, and the like. Display 108 may be configured to provide a way for a user to view and / or interact with information, including but not limited to ultrasound image 104, 3D cardiac model 126, 3D catheter model 128, 3D stent model 136, and / or the like. In some embodiments, display 108 may be implemented in any user device 118 disclosed in the entirety of this disclosure. In some embodiments, display 108 may include different technologies, such as liquid crystal display (LCD), a light-emitting diode (LED), organic light-emitting diode (OLED), plasma, projection, touch screen, and / or the like. In some embodiments, display 108 may include varying resolutions, sizes, and aspect ratios. With continued reference to FIG.1, in some embodiments, display 108 may include a plurality of display windows 166a-b. For the purposes of this disclosure, a “display window” is a defined visual area within a display. As a non-limiting example, display 108 may include a first display window 166a. In some embodiments, first display window 166a may be configured to display at least an ultrasound image 104, and the like. In some embodiments, display 108 may include a second display window 166b. In some embodiments, second display window 166b may be configured to display 3D cardiac model 126, 3D catheter model 128, 3D stent model 136, superimposed model 168, path model 170, and the like. With continued reference to FIG.1, in some embodiments, second display window 166b may include a user interface 172. For the purposes of this disclosure, a “user interface” is a means by which a user and a computer system interact; for example through the use of input devices and software. A user interface 172 may include a graphical user interface (GUI), command line interface (CLI), menu-driven user interface, touch user interface, voice user interface (VUI), form-based user interface, any combination thereof and the like. In some embodiments, a user may interact with user interface 172 in virtual reality. In some embodiments, a user may interact with the use interface using a computing device distinct from and communicatively connected to at least a processor 110. For example, a smart phone, smart, 67 Attorney Docket No.1518-194PCT1 tablet, or laptop operated by a user. With continued reference to FIG.1, in an embodiment, user interface 172 may include a graphical user interface. A “graphical user interface,” as used herein, is a graphical form of user interface that allows users to interact with electronic devices. In some embodiments, GUI may include icons, menus, other visual indicators or representations (graphics), audio indicators such as primary notation, and display information and related user controls. A menu may contain a list of choices and may allow users to select one from them. A menu bar may be displayed horizontally across the screen such as pull-down menu. When any option is clicked in this menu, then the pull-down menu may appear. A menu may include a context menu that appears only when the user performs a specific action. An example of this is pressing the right mouse button. When this is done, a menu may appear under the cursor. Files, programs, web pages and the like may be represented using a small picture in a graphical user interface. For example, links to decentralized platforms as described in this disclosure may be incorporated using icons. Using an icon may be a fast way to open documents, run programs etc. because clicking on them yields instant access. With continued reference to FIG.1, displaying at least a 3D cardiac model 126, at least a 3D catheter model 128 and at least a 3D stent model 136 includes superimposing the at least a 3D stent model 136 onto the at least a 3D cardiac model 126. In some embodiments, processor 110 may generate a superimposed model 168 by superimposing at least a 3D stent model 136 onto at least a 3D cardiac model 126. For the purposes of this disclosure, a “superimposed model” is a three-dimensional representation of a 3D model superimposed onto at least a 3D cardiac model. As a non-limiting example, superimposed model 168 may include 3D representation of stent implanted at coronary artery of a patient’s heart. For the purposes of this disclosure, “superimpose” is the process of overlaying an image onto another image. In some embodiments, processor 110 may be further configured to determine a position datum 174. For the purpose of this disclosure, a “position datum” is a data element related to a position that a 3D stent model can be superimposed on to a 3D cardiac model. As a non-limiting example, position datum 174 may include a location of an artery within 3D cardiac model 126. In an embodiment, position datum 174 may include a position of superimposed model 168 in a field coordinate system. As a non-limiting example, position datum 174 may be obtained using a machine vision system. As another non-limiting example, processor 110 may determine position datum 174 as a 68 Attorney Docket No.1518-194PCT1 function of cardiac featuring datum 120, wherein the cardiac featuring datum 120 may include density datum 176. For the purposes of this disclosure, a “density datum” is a data element that represents the extent of arterial clogging or narrowing. In some embodiments, processor 110 may determine density datum 176 using a machine vision system, or any imaging processing module described in this disclosure. In a non-limiting example, processor 110 may determine position datum 174 by choosing a location of a coronary artery that has the highest density datum 176. In some embodiments, determining position datum 174 may include determining the position datum 174 as a function of a user input 178 received from a user interface 172 of at least a display 108. For the purposes of this disclosure, a “user input” is any data, command, or instruction provided by a user. As a non-limiting example, user may include a clinician or operator. As a non-limiting example, user may manually determine position datum 174 (user input 178). In a non-limiting example, user may manipulate user interface 172 of display 108 to click or touch one location of coronary artery within ultrasound image 104 or 3D cardiac model 126 to input position datum 174 (user input 178). In some embodiments, processor 110 may superimpose at least a 3D stent model 136 onto at least a 3D cardiac model 126 as a function of position datum 174. In some embodiments, superimposed model 168 may be stored in a stent database 116. In some embodiments, superimposed model 168 may be retrieved from stent database 116. With continued reference to FIG.1, in some embodiments, superimposing at least a 3D stent model 136 onto at least a 3D cardiac model 126 may include determining an optimal path 180 for a placement of the at least a 3D stent model within the at least a 3D cardiac model 126, generating a path model 170 for the optimal path 180 and superimposing the path model 170 onto the at least a 3D cardiac model 126. For the purposes of this disclosure, an “optimal path” is a trajectory or route that minimizes risk and maximizes accuracy for the placement of a device within a subject. In some embodiments, optimal path 180 may be determined by evaluating anatomical constraints (cardiac featuring datum 120 and / or artery featuring datum 138), stent specifications, and the like. In some embodiments, processor 110 may determine optimal path 180 using a graph-based pathfinding that represents 3D cardiac model 126 as a graph where nodes represent points in space and edges represent potential paths between them. As a non- limiting example, processor 110 may use Dijkstra's algorithm, A-star algorithm, and the like to determine optimal path 180. In some embodiments, user may manually generate optimal path 180. For example, and without limitation, user may manipulate user interface 172 to generate 69 Attorney Docket No.1518-194PCT1 optimal path 180. With continued reference to FIG.1, in some embodiments, processor 110 may determine optimal path 180 through the use of machine-learning module. In some embodiments, processor 110 may be configured to generate path training data. In a non-limiting example, path training data may include historical data from previous successful procedures of placement of stents. In another non-limiting example, path training data may include correlations between exemplary 3D cardiac models, exemplary 3D stent models and exemplary optimal paths. In some embodiments, path training data may be stored in stent database 116. In some embodiments, path training data may be received from one or more users, stent database 116, external computing devices, and / or previous iterations of processing. As a non-limiting example, path training data may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, which may be stored in memory and / or stored in stent database 116, where the instructions may include labeling of training examples. In some embodiments, path training data may be updated iteratively on a feedback loop. As a non-limiting example, processor 110 may update path training data iteratively through a feedback loop as a function of ultrasound image 104, 3D cardiac models 126, 3D stent models 128, cardiac featuring datum 120, image segment 124, or the like. In some embodiments, processor 110 may be configured to generate a path machine-learning model. In a non-limiting example, generating path machine- learning model may include training, retraining, or fine-tuning path machine-learning model using path training data or updated path training data. In some embodiments, processor 110 may be configured to determine optimal path 180 using path machine-learning model (i.e., trained or updated path machine-learning model). In some embodiments, generating training data and training machine-learning models may be simultaneous. With continued reference to FIG.1, for the purposes of this disclosure, a “path model” is a computational representation of an optimal path. In a non-limiting example, path model 170 may provide visual and numerical guidance for navigating the delivery system (catheter) and deploying stent. As a non-limiting example, path model 170 may include trajectory representing a series of connected points, vectors, or curves defining Stent’s path through a heart. In some embodiments, generating path model 170 may include dividing optimal path 180 into a series of path points. As a non-limiting example, path points may include start point that can be an entry point of a delivery system (e.g., the femoral vein, transseptal puncture site or septum fossa 70 Attorney Docket No.1518-194PCT1 ovalis). As another non-limiting example, path points may include intermediate points that are points along the trajectory through cardiac structures such as the left atrium and right atrium. As another non-limiting example, path points may include end point that is a final position at the coronary artery where the stent will be deployed. In some embodiments, generating path model 170 may include annotating each path point with coordinates (e.g., [x, y, z] positions in 3D cardiac model 126) and orientation. In some embodiments, generating path model 170 may include interpolating optimal path using mathematical interpolation (e.g., cubic splines or Bézier curves) to connect path points. With continued reference to FIG.1, in some embodiments, displaying at least a 3D cardiac model 126, at least a 3D catheter model 128 and at least a 3D stent model 136 may include generating a notification datum 182 as a function of a position datum 174 and at least a 3D stent model 136. For the purposes of this disclosure, a “notification datum” is a data element that conveys information to a user. As a non-limiting example, notification datum 182 may include visual, auditory, textual, vibration indications, and the like. In a non-limiting example, notification datum 182 may indicate an alignment between planned and executed stent placement. In some embodiments, processor 110 may determine when the spatial coordinates, orientation, or other relevant parameters of an actual stent placement (that can be represented by 3D stent model 136) fall within predefined tolerances of the suggested position (position datum 174). In some embodiments, processor 110 may transmit notification datum 182 to display 108 of user device 118. With continued reference to FIG.1, in some embodiments, displaying at least a portion of at least a 3D cardiac model 126 and at least a 3D stent model 136 may include generating a pseudo TEE frame 184 as a function of the at least a 3D cardiac model 126 and the view label 152 and superimposing the at least a 3D stent model 136 on to the pseudo TEE frame 184. For the purposes of this disclosure, a “pseudo transesophageal echocardiogram frame” is a two dimensional (2D) slice of a 3D cardiac model at a given view angle or position. In some embodiments, processor 110 may generate pseudo TEE frame 184 by computationally projecting 3D cardiac model 126 onto a 2D plane using view labels 152 or imaging parameters to replicate the perspective, depth, and anatomical detail in an actual TEE frame (ultrasound image 104). In some embodiments, pseudo TEE frame 184 may include color Doppler overlays to simulate markers indicating optimal path 180 or position datum 174. In some embodiments, processor 110 71 Attorney Docket No.1518-194PCT1 may display pseudo TEE frame 184 through display 108. Referring now to FIG.2, an exemplary display 200 used during planning of implantation of a stent, according to some embodiments. In some embodiments, display 200 may be implemented in any user device 118. In some embodiments, display 200 may include a first display window 166a displaying at least an ultrasound image 104. In some embodiments, display 200 may include a second display window 166b displaying path model 170, 3D cardiac model 126, 3D stent model 136, and the like. In some embodiments, second display window 166b may include a superimposed model 168. In some embodiments, user may interact with second display window 166b to manipulated with displayed models. In a non-limiting example, user may try placing different sizes and types of stent on ostium within 3D cardiac model 126. For example, second display window 166b may show a suggested location. A suggested location may include rendering superimposed model 168 at a suggested location. In some embodiments, display 200 may allow user to toggle between one or more, suggested location, displaying the superimposed model 168 at the selected location. Referring now to FIG.3, an exemplary display 300 used during implantation of a stent, according to some embodiments. In some embodiments, display 300 may be implemented in any user device 118. In some embodiments, display 300 may include a first display window 166a displaying at least an ultrasound image 104. In some embodiments, display 300 may include a second display window 166b displaying superimposed model 168, path model 170, 3D cardiac model 126, 3D stent model 136, and the like. In some embodiments, user may interact with second display window 166b to manipulated with displayed models. In a non-limiting example, user may be placing stent to ostium of a patient’s heart with a guide (superimposed model 168, path model 170, and the like) displayed on a display 300. Now referring to FIG.4, an exemplary embodiment of a 3D VOR 400 is illustrated.3D VOR 400 may be used to represent 3D object 404. In an embodiment, 3D VOR 400 may divide a 3D space 408 into a grid of one or more cubic units e.g., voxels 412, wherein each voxel 412 represents a specific volume within 3D space 408. In a non-limiting example, 3D object 404 may include a structure pertaining to a subject. Still referring to FIG.4, in some cases, each voxel 412 may act as a basic building block. In a non-limiting example, each voxel 412 may be configured to represent a discrete portion of 3D space 408. In an embodiment, each voxel 412 may include a presence indicator as 72 Attorney Docket No.1518-194PCT1 described above with reference to FIG.1, which denotes whether the voxel is occupied or unoccupied. In such embodiment, the binary or continuous value may allow 3D VOR 400 to map the presence or absence of material within each voxel 412, creating a granular representation of 3D object 404. With continued reference to FIG.4, in some cases, the resolution of 3D VOR 400 may be determined by the size and number of voxels within the grid. In a non-limiting example, smaller voxel may provide a higher resolution, capturing finer details, while larger voxels offer a more generalized representation. Still referring to FIG.4, in an embodiment, voxels 412 may be arranged in a regular pattern along three axis 416a-c, each pointing a distinct direction. In a non-limiting example, voxels 412 may be arranged along x, y, and z axes, wherein such arrangement may facilitate efficient manipulation and rendering of the 3D object 404. In some cases, cardiac feature datums 420a-c such as, without limitation, edges, surfaces, textures, and any other cardiac feature datums as described above with reference to FIG.1, may be extracted from 3D VOR 400 by analyzing the relationships and patterns between neighboring voxels. Now referring to FIG.5, a schematic of an exemplary transesophageal echocardiogram (TEE) procedure 500 is shown. In some cases, TEE procedure 500 may be performed during another procedure for instance heart surgery. According to some embodiments, a patient 504 has an endoscope 508, with an ultrasonic transducer 512, inserted into his esophagus 516. As one’s esophagus 516 is proximal one’s heart 520, ultrasonic transducer 512 may generate echocardiograms. Still referring to FIG.5, in some embodiments, transesophageal echocardiography (TEE) may provide superior imaging quality than intracardiac echocardiography (ICE), as larger ultrasound transducers 512 may be placed within the esophagus 516 than within heart 520. In some cases, ultrasound transducers may be substantially miniaturized to fit within heart 520, as in ICE catheters. As esophagus 516 may be proximal to heart 520, TEE may provide a clear image of various heart structures without needing vascular access (as commonly required by ICE). Additionally, TEE may be performed without obstructing patient’s 504 ribcage and intermediary tissues (as commonly required by transthoracic echocardiography [TTE]). In some cases, TEE images may also provide information associated with angle of acquisition. Angle of acquisition may be an angle of TEE probe with respect to esophagus 516 (e.g., esophageal axis). 73 Attorney Docket No.1518-194PCT1 Still referring to FIG.5, in some embodiments, TEE echocardiogram data, including images showing heart structures and, in some cases, angle of acquisition, may be used as input to any machine learning process described in this application, for instance with reference to FIGS.1 – 4 and 6 - 13. For instance TEE echocardiogram data may be used to reconstruct 3D heart models. In some cases, TEE echocardiogram data may be input into a machine learning model that outputs a 3D heart model (e.g., 3D mesh model and / or statistical shape model). Still referring to FIG.5, in some embodiments, TEE may be a preferred imaging modality for structural heart interventions, such as without limitation a stent placement. In some cases, technology and improvements described in this disclosure permit creation and / or modification of a 3D heart mesh from TEE data to aid in planning implant size selection, as well as to guide implantation procedures. In some cases, virtual placement of a 3D model of a candidate implant (such as without limitation a stent) can be simulated on a 3D heart model generated by any method described in this disclosure. This novel and improved functionality may validate appropriate size and placement of implants within heart 520, as well as other organs within body of patient 504. For example, in the context of electrophysiology procedures, TEE procedure 500 can be used to create heart anatomical models that can be used as reference for electroanatomic mapping, and guidance of catheters for coronary heart disease treatment procedures (such as without limitation navigating a catheter through arteries). Still referring to FIG.5, in some embodiments, applications described with reference to TEE procedure 500 above can be extended for use with TTE and point of care ultrasound (POCUS). In some cases, both TTE and POCUS may acquire ultrasound images of chest / surface of patient 504. In some cases, TTE and POCUS data may be used as an input (and / or training data) for any machine learning process described in this disclosure, for instance with reference to FIGS.1 – 4 and 6 - 12. In some cases, use of TTE and / or POCUS data (in machine learning processes described in this disclosure) may require adjustment in ultrasound acquisition parameters and positions to acquire a sufficient number of frames for 3D reconstruction. In some cases, TTE and POCUS may offer improved accessibility (with POCUS being portable / mobile as well) and non-invasive 3D heart modeling, often without anesthesia or sedation, compared to catheterized 3D heart modeling commonly performed today for electroanatomical mapping and ablation procedures. Referring now to FIG.6, an exemplary embodiment of a machine-learning module 600 74 Attorney Docket No.1518-194PCT1 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and / or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 604 to generate an algorithm instantiated in hardware or software logic, data structures, and / or functions that will be performed by a computing device / module to produce outputs 608 given data provided as inputs 612; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Still referring to FIG.6, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 604 may include a plurality of data entries, also known as “training examples,” each entry representing a set of data elements that were recorded, received, and / or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 604 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 604 according to various correlations; correlations may indicate causative and / or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data 604 may be formatted and / or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non- limiting example, training data 604 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 604 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 604 may be provided in fixed-length formats, formats linking positions of data to categories 75 Attorney Docket No.1518-194PCT1 such as comma-separated value (CSV) formats and / or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data. Alternatively or additionally, and continuing to refer to FIG.6, training data 604 may include one or more elements that are not categorized; that is, training data 604 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and / or other processes may sort training data 604 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and / or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine- learning algorithms, and / or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 604 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 604 used by machine-learning module 600 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example, input data may include ultrasound image 104, 3D cardiac model 126, 3D catheter model 128, 3D stent model 136, TEE angle datum 158, cardiac featuring datum 120, 3D point cloud 130, artery featuring datum 138, optimal path 180, IFU data 164, user input 178, patient data 142, position datum 174, and the like. As a non- limiting illustrative example, output data may include 3D cardiac model 126, 3D catheter model 128, 3D stent model 136, TEE angle datum 158, cardiac featuring datum 120, 3D point cloud 130, artery featuring datum 138, optimal path 180, superimposed model 168, stent datum 140, placement datum 150, path model 170, view label 152, image inquiry datum 162, notification datum 182, placement datum 150, position datum 174, and the like. Further referring to FIG.6, training data may be filtered, sorted, and / or selected using one 76 Attorney Docket No.1518-194PCT1 or more supervised and / or unsupervised machine-learning processes and / or models as described in further detail below; such models may include without limitation a training data classifier 616. Training data classifier 616 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and / or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and / or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 600 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and / or any module and / or component operating thereon derives a classifier from training data 604. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and / or neural network-based classifiers. As a non-limiting example, training data classifier 616 may classify elements of training data to a patient cohort related to patient’s age, gender, medical experience, medical record, and the like. Still referring to FIG.6, computing device may be configured to generate a classifier using a Naïve Bayes classification algorithm. Naïve Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naïve Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naïve Bayes classification algorithm may be based on Bayes Theorem expressed as P(A / B)= P(B / A) P(A)÷P(B), where P(A / B) is the probability of hypothesis A given data B also known as posterior probability; P(B / A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naïve Bayes algorithm may be 77 Attorney Docket No.1518-194PCT1 generated by first transforming training data into a frequency table. Computing device may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Computing device may utilize a naïve Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naïve Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naïve Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naïve Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary. With continued reference to FIG.6, computing device may be configured to generate a classifier using a K-nearest neighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used in this disclosure, includes a classification method that utilizes feature similarity to analyze how closely out-of-sample- features resemble training data to classify input data to one or more clusters and / or categories of features as represented in training data; this may be performed by representing both training data and input data in vector forms, and using one or more measures of vector similarity to identify classifications within training data, and to determine a classification of input data. K-nearest neighbors algorithm may include specifying a K-value, or a number directing the classifier to select the k most similar entries training data to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and / or iteratively to generate a classifier that may be used to classify input data as further samples. For instance, an initial set of samples may be performed to cover an initial heuristic and / or “first guess” at an output and / or relationship, which may be seeded, without limitation, using expert input received according to any process as described herein. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data. Heuristic may include selecting some number of highest-ranking associations and / or training data elements. With continued reference to FIG.6, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like. Each vector output may be represented, without limitation, as an n- tuple of values, where n is at least two values. Each value of n-tuple of values may represent a 78 Attorney Docket No.1518-194PCT1 measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and / or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 9, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a lengthattribute l as derived using a Pythagorean norm: = , where ai is attribute number i ofthe vector.. Scaling and / or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values. With further reference to FIG.6, training examples for use as training data may be selected from a population of potential examples according to cohorts relevant to an analytical problem to be solved, a classification task, or the like. Alternatively or additionally, training data may be selected to span a set of likely circumstances or inputs for a machine-learning model and / or process to encounter when deployed. For instance, and without limitation, for each category of input data to a machine-learning process or model that may exist in a range of values in a population of phenomena such as images, user data, process data, physical data, or the like, a computing device, processor, and / or machine-learning model may select training examples representing each possible value on such a range and / or a representative sample of values on such a range. Selection of a representative sample may include selection of training examples in 79 Attorney Docket No.1518-194PCT1 proportions matching a statistically determined and / or predicted distribution of such values according to relative frequency, such that, for instance, values encountered more frequently in a population of data so analyzed are represented by more training examples than values that are encountered less frequently. Alternatively or additionally, a set of training examples may be compared to a collection of representative values in a database and / or presented to a user, so that a process can detect, automatically or via user input, one or more values that are not included in the set of training examples. Computing device, processor, and / or module may automatically generate a missing training example; this may be done by receiving and / or retrieving a missing input and / or output value and correlating the missing input and / or output value with a corresponding output and / or input value collocated in a data record with the retrieved value, provided by a user and / or other device, or the like. Continuing to refer to FIG.6, computer, processor, and / or module may be configured to preprocess training data. “Preprocessing” training data, as used in this disclosure, is transforming training data from raw form to a format that can be used for training a machine learning model. Preprocessing may include sanitizing, feature selection, feature scaling, data augmentation and the like. Still referring to FIG.6, computer, processor, and / or module may be configured to sanitize training data. “Sanitizing” training data, as used in this disclosure, is a process whereby training examples are removed that interfere with convergence of a machine-learning model and / or process to a useful result. For instance, and without limitation, a training example may include an input and / or output value that is an outlier from typically encountered values, such that a machine-learning algorithm using the training example will be adapted to an unlikely amount as an input and / or output; a value that is more than a threshold number of standard deviations away from an average, mean, or expected value, for instance, may be eliminated. Alternatively or additionally, one or more training examples may be identified as having poor quality data, where “poor quality” is defined as having a signal to noise ratio below a threshold value. Sanitizing may include steps such as removing duplicative or otherwise redundant data, interpolating missing data, correcting data errors, standardizing data, identifying outliers, and the like. In a nonlimiting example, sanitization may include utilizing algorithms for identifying duplicate entries or spell-check algorithms. As a non-limiting example, and with further reference to FIG.6, images used to train an 80 Attorney Docket No.1518-194PCT1 image classifier or other machine-learning model and / or process that takes images as inputs or generates images as outputs may be rejected if image quality is below a threshold value. For instance, and without limitation, computing device, processor, and / or module may perform blur detection, and eliminate one or more Blur detection may be performed, as a non-limiting example, by taking Fourier transform, or an approximation such as a Fast Fourier Transform (FFT) of the image and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of the image; numbers of high-frequency values below a threshold level may indicate blurriness. As a further non-limiting example, detection of blurriness may be performed by convolving an image, a channel of an image, or the like with a Laplacian kernel; this may generate a numerical score reflecting a number of rapid changes in intensity shown in the image, such that a high score indicates clarity and a low score indicates blurriness. Blurriness detection may be performed using a gradient-based operator, which measures operators based on the gradient or first derivative of an image, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. Blur detection may be performed using Wavelet -based operator, which takes advantage of the capability of coefficients of the discrete wavelet transform to describe the frequency and spatial content of images. Blur detection may be performed using statistics-based operators take advantage of several image statistics as texture descriptors in order to compute a focus level. Blur detection may be performed by using discrete cosine transform (DCT) coefficients in order to compute a focus level of an image from its frequency content. Continuing to refer to FIG.6, computing device, processor, and / or module may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and / or process has one or more inputs and / or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples’ elements to be used as or compared to inputs and / or outputs may be modified to have such a number of units of data. For instance, a computing device, processor, and / or module may convert a smaller number of units, such as in a low pixel count image, into a desired number of units, for instance by upsampling and interpolating. As a non-limiting example, a low pixel count image may have 90 pixels, however a desired number of pixels may be 118. Processor may interpolate the low pixel count image to convert the 90 pixels into 118 pixels. It should also be noted that one of ordinary skill in the art, upon reading this disclosure, would 81 Attorney Docket No.1518-194PCT1 know the various methods to interpolate a smaller number of data units such as samples, pixels, bits, or the like to a desired number of such units. In some instances, a set of interpolation rules may be trained by sets of highly detailed inputs and / or outputs and corresponding inputs and / or outputs downsampled to smaller numbers of units, and a neural network or other machine learning model that is trained to predict interpolated pixel values using the training data. As a non-limiting example, a sample input and / or output, such as a sample picture, with sample- expanded data units (e.g., pixels added between the original pixels) may be input to a neural network or machine-learning model and output a pseudo replica sample-picture with dummy values assigned to pixels between the original pixels based on a set of interpolation rules. As a non-limiting example, in the context of an image classifier, a machine-learning model may have a set of interpolation rules trained by sets of highly detailed images and images that have been downsampled to smaller numbers of pixels, and a neural network or other machine learning model that is trained using those examples to predict interpolated pixel values in a facial picture context. As a result, an input with sample-expanded data units (the ones added between the original data units, with dummy values) may be run through a trained neural network and / or model, which may fill in values to replace the dummy values. Alternatively or additionally, processor, computing device, and / or module may utilize sample expander methods, a low-pass filter, or both. As used in this disclosure, a “low-pass filter” is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and / or module may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units. In some embodiments, and with continued reference to FIG.6, computing device, processor, and / or module may down-sample elements of a training example to a desired lower number of data elements. As a non-limiting example, a high pixel count image may have 256 pixels, however a desired number of pixels may be 118. Processor may down-sample the high pixel count image to convert the 256 pixels into 118 pixels. In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as “compression,” and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing 82 Attorney Docket No.1518-194PCT1 and / or anti-imaging filters, and / or low-pass filters, may be used to clean up side-effects of compression. Further referring to FIG.6, feature selection includes narrowing and / or filtering training data to exclude features and / or elements, or training data including such elements, that are not relevant to a purpose for which a trained machine-learning model and / or algorithm is being trained, and / or collection of features and / or elements, or training data including such elements, on the basis of relevance or utility for an intended task or purpose for a trained machine-learning model and / or algorithm is being trained. Feature selection may be implemented, without limitation, using any process described in this disclosure, including without limitation using training data classifiers, exclusion of outliers, or the like. With continued reference to FIG.6, feature scaling may include, without limitation, normalization of data entries, which may be accomplished by dividing numerical fields by norms thereof, for instance as performed for vector normalization. Feature scaling may include absolute maximum scaling, wherein each quantitative datum is divided by the maximum absolute value of all quantitative data of a set or subset of quantitative data. Feature scaling may include min-max scaling, in which each value has a minimum value in a set or subset of values subtracted therefrom, with the result divided by the range of the values, give maximum value in the set orsubset : = . Feature scaling may include mean normalization, whichinvolves use a set and / or subset of values, with maximum andminimum values: = . Feature scaling may include standardization, where adifference between and divided by a standard deviation of a set or subset of values:=. Scaling may be performed using a median value of a set or subset range (IQR), which represents the difference between the 25thpercentilevalue and the 50th percentile value (or closest values thereto by a rounding protocol), such as:=. Persons skilled in the art, upon reviewing the entirety of this disclosure, willbe aware alternative or additional approaches that may be used for feature scaling. Further referring to FIG.6, computing device, processor, and / or module may be configured to perform one or more processes of data augmentation. “Data augmentation” as used in this disclosure is addition of data to a training set using elements and / or entries already in the dataset. Data augmentation may be accomplished, without limitation, using interpolation, 83 Attorney Docket No.1518-194PCT1 generation of modified copies of existing entries and / or examples, and / or one or more generative AI processes, for instance using deep neural networks and / or generative adversarial networks; generative processes may be referred to alternatively in this context as “data synthesis” and as creating “synthetic data.” Augmentation may include performing one or more transformations on data, such as geometric, color space, affine, brightness, cropping, and / or contrast transformations of images. Still referring to FIG.6, machine-learning module 600 may be configured to perform a lazy-learning process 620 and / or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and / or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and / or “first guess” at an output and / or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 604. Heuristic may include selecting some number of highest-ranking associations and / or training data 604 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy- learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below. Alternatively or additionally, and with continued reference to FIG.6, machine-learning processes as described in this disclosure may be used to generate machine-learning models 624. A “machine-learning model,” as used in this disclosure, is a data structure representing and / or instantiating a mathematical and / or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model 624 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting 84 Attorney Docket No.1518-194PCT1 example, a machine-learning model 624 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data 604 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Still referring to FIG.6, machine-learning algorithms may include at least a supervised machine-learning process 628. At least a supervised machine-learning process 628, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to generate one or more data structures representing and / or instantiating one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include input data may include ultrasound image 104, 3D cardiac model 126, 3D catheter model 128, 3D stent model 136, TEE angle datum 158, cardiac featuring datum 120, 3D point cloud 130, artery featuring datum 138, optimal path 180, IFU data 164, user input 178, patient data 142, position datum 174, and the like as described above as inputs, 3D cardiac model 126, 3D catheter model 128, 3D stent model 136, TEE angle datum 158, cardiac featuring datum 120, 3D point cloud 130, artery featuring datum 138, optimal path 180, superimposed model 168, stent datum 140, placement datum 150, path model 170, view label 152, image inquiry datum 162, notification datum 182, placement datum 150, position datum 174, and the like as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and / or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 604. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various 85 Attorney Docket No.1518-194PCT1 possible variations of at least a supervised machine-learning process 628 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above. With further reference to FIG.6, training a supervised machine-learning process may include, without limitation, iteratively updating coefficients, biases, weights based on an error function, expected loss, and / or risk function. For instance, an output generated by a supervised machine-learning model using an input example in a training example may be compared to an output example from the training example; an error function may be generated based on the comparison, which may include any error function suitable for use with any machine-learning algorithm described in this disclosure, including a square of a difference between one or more sets of compared values or the like. Such an error function may be used in turn to update one or more weights, biases, coefficients, or other parameters of a machine-learning model through any suitable process including without limitation gradient descent processes, least-squares processes, and / or other processes described in this disclosure. This may be done iteratively and / or recursively to gradually tune such weights, biases, coefficients, or other parameters. Updating may be performed, in neural networks, using one or more back-propagation algorithms. Iterative and / or recursive updates to weights, biases, coefficients, or other parameters as described above may be performed until currently available training data is exhausted and / or until a convergence test is passed, where a “convergence test” is a test for a condition selected as indicating that a model and / or weights, biases, coefficients, or other parameters thereof has reached a degree of accuracy. A convergence test may, for instance, compare a difference between two or more successive errors or error function values, where differences below a threshold amount may be taken to indicate convergence. Alternatively or additionally, one or more errors and / or error function values evaluated in training iterations may be compared to a threshold. Still referring to FIG.6, a computing device, processor, and / or module may be configured to perform method, method step, sequence of method steps and / or algorithm described in reference to this figure, in any order and with any degree of repetition. For instance, a computing device, processor, and / or module may be configured to perform a single step, sequence and / or algorithm repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or 86 Attorney Docket No.1518-194PCT1 outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. A computing device, processor, and / or module may perform any step, sequence of steps, or algorithm in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing. Further referring to FIG.6, machine learning processes may include at least an unsupervised machine-learning processes 632. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and / or correlation provided in the data. Unsupervised processes 632 may not require a response variable; unsupervised processes 632may be used to find interesting patterns and / or inferences between variables, to determine a degree of correlation between two or more variables, or the like. Still referring to FIG.6, machine-learning module 600 may be designed and configured to create a machine-learning model 624 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g., a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to 87 Attorney Docket No.1518-194PCT1 the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output / actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure. Continuing to refer to FIG.6, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminant analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including, without limitation, support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and / or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine- learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized trees, AdaBoost, gradient tree boosting, and / or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes. Still referring to FIG.6, a machine-learning model and / or process may be deployed or instantiated by incorporation into a program, apparatus, system and / or module. For instance, and without limitation, a machine-learning model, neural network, and / or some or all parameters 88 Attorney Docket No.1518-194PCT1 thereof may be stored and / or deployed in any memory or circuitry. Parameters such as coefficients, weights, and / or biases may be stored as circuit-based constants, such as arrays of wires and / or binary inputs and / or outputs set at logic “1” and “0” voltage levels in a logic circuit to represent a number according to any suitable encoding system including twos complement or the like or may be stored in any volatile and / or non-volatile memory. Similarly, mathematical operations and input and / or output of data to or from models, neural network layers, or the like may be instantiated in hardware circuitry and / or in the form of instructions in firmware, machine-code such as binary operation code instructions, assembly language, or any higher- order programming language. Any technology for hardware and / or software instantiation of memory, instructions, data structures, and / or algorithms may be used to instantiate a machine- learning process and / or model, including without limitation any combination of production and / or configuration of non-reconfigurable hardware elements, circuits, and / or modules such as without limitation ASICs, production and / or configuration of reconfigurable hardware elements, circuits, and / or modules such as without limitation FPGAs, production and / or of non- reconfigurable and / or configuration non-rewritable memory elements, circuits, and / or modules such as without limitation non-rewritable ROM, production and / or configuration of reconfigurable and / or rewritable memory elements, circuits, and / or modules such as without limitation rewritable ROM or other memory technology described in this disclosure, and / or production and / or configuration of any computing device and / or component thereof as described in this disclosure. Such deployed and / or instantiated machine-learning model and / or algorithm may receive inputs from any other process, module, and / or component described in this disclosure, and produce outputs to any other process, module, and / or component described in this disclosure. Continuing to refer to FIG.6, any process of training, retraining, deployment, and / or instantiation of any machine-learning model and / or algorithm may be performed and / or repeated after an initial deployment and / or instantiation to correct, refine, and / or improve the machine- learning model and / or algorithm. Such retraining, deployment, and / or instantiation may be performed as a periodic or regular process, such as retraining, deployment, and / or instantiation at regular elapsed time periods, after some measure of volume such as a number of bytes or other measures of data processed, a number of uses or performances of processes described in this disclosure, or the like, and / or according to a software, firmware, or other update schedule. 89 Attorney Docket No.1518-194PCT1 Alternatively or additionally, retraining, deployment, and / or instantiation may be event-based, and may be triggered, without limitation, by user inputs indicating sub-optimal or otherwise problematic performance and / or by automated field testing and / or auditing processes, which may compare outputs of machine-learning models and / or algorithms, and / or errors and / or error functions thereof, to any thresholds, convergence tests, or the like, and / or may compare outputs of processes described herein to similar thresholds, convergence tests or the like. Event-based retraining, deployment, and / or instantiation may alternatively or additionally be triggered by receipt and / or generation of one or more new training examples; a number of new training examples may be compared to a preconfigured threshold, where exceeding the preconfigured threshold may trigger retraining, deployment, and / or instantiation. Still referring to FIG.6, retraining and / or additional training may be performed using any process for training described above, using any currently or previously deployed version of a machine-learning model and / or algorithm as a starting point. Training data for retraining may be collected, preconditioned, sorted, classified, sanitized or otherwise processed according to any process described in this disclosure. Training data may include, without limitation, training examples including inputs and correlated outputs used, received, and / or generated from any version of any system, module, machine-learning model or algorithm, apparatus, and / or method described in this disclosure; such examples may be modified and / or labeled according to user feedback or other processes to indicate desired results, and / or may have actual or measured results from a process being modeled and / or predicted by system, module, machine-learning model or algorithm, apparatus, and / or method as “desired” results to be compared to outputs for training processes as described above. Redeployment may be performed using any reconfiguring and / or rewriting of reconfigurable and / or rewritable circuit and / or memory elements; alternatively, redeployment may be performed by production of new hardware and / or software components, circuits, instructions, or the like, which may be added to and / or may replace existing hardware and / or software components, circuits, instructions, or the like. Further referring to FIG.6, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 636. A “dedicated hardware unit,” for the purposes of this figure, is a hardware component, circuit, or the like, aside from a principal control circuit and / or processor performing method steps as described in this disclosure, that is 90 Attorney Docket No.1518-194PCT1 specifically designated or selected to perform one or more specific tasks and / or processes described in reference to this figure, such as without limitation preconditioning and / or sanitization of training data and / or training a machine-learning algorithm and / or model. A dedicated hardware unit 636 may include, without limitation, a hardware unit that can perform iterative or massed calculations, such as matrix-based calculations to update or tune parameters, weights, coefficients, and / or biases of machine-learning models and / or neural networks, efficiently using pipelining, parallel processing, or the like; such a hardware unit may be optimized for such processes by, for instance, including dedicated circuitry for matrix and / or signal processing operations that includes, e.g., multiple arithmetic and / or logical circuit units such as multipliers and / or adders that can act simultaneously and / or in parallel or the like. Such dedicated hardware units 636 may include, without limitation, graphical processing units (GPUs), dedicated signal processing modules, FPGA or other reconfigurable hardware that has been configured to instantiate parallel processing units for one or more specific tasks, or the like, A computing device, processor, apparatus, or module may be configured to instruct one or more dedicated hardware units 636 to perform one or more operations described herein, such as evaluation of model and / or algorithm outputs, one-time or iterative updates to parameters, coefficients, weights, and / or biases, and / or any other operations such as vector and / or matrix operations as described in this disclosure. Referring now to FIG.7, an exemplary embodiment of neural network 700 is illustrated. A neural network 700 also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes 704, one or more intermediate layers 708, and an output layer of nodes 712. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Connections may run solely from input nodes toward output nodes in a “feed-forward” network, or may feed outputs of one layer back to inputs of the same or a different layer in a “recurrent network.” As a further non- 91 Attorney Docket No.1518-194PCT1 limiting example, a neural network may include a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. A “convolutional neural network,” as used in this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. Referring now to FIG.8, an exemplary embodiment of a node 800 of a neural network is illustrated. A node may include, without limitation, a plurality of inputs xi that may receive numerical values from inputs to a neural network containing the node and / or from other nodes. Node may perform one or more activation functions to produce its output given one or more inputs, such as without limitation computing a binary step function comparing an input to a threshold value and outputting either a logic 1 or logic 0 output or something equivalent, a linear activation function whereby an output is directly proportional to the input, and / or a non-linear activation function, wherein the output is not proportional to the input. Non-linear activationfunctions may include, without limitation, a sigmoid function of the form ( ) = given input x, a tanh (hyperbolic tangent) function, of the form , a tanh suchas ( ) = tanh ( ), a rectified linear unit function such as ( ) = max (0, ), a “leaky” and / or“parametric” rectified linear unit function such as ( ) = max ( , ) for some a, anexponential linear units function such as ( ) = 0( 1) < 0 for some value of(this function may be replaced and / or weighted by its own derivative in some embodiments), asoftmax function such as ( ) = where the inputs to an instant layer are , a swish function such as ( ) = ( ), a Gaussian error linear unit function such as f(x) =1 + tanh ( 2 / ( + )) for some values of a, b, and r, and / or a scaled exponential linearunit ( 1) < 00 . Fundamentally, there is no limit to thenature of functions of inputs xithat may be used as activation functions. As a non-limiting and illustrative example, node may perform a weighted sum of inputs using weights wi that are multiplied by respective inputs xi. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer 92 Attorney Docket No.1518-194PCT1 that is independent of the input to the layer. The weighted sum may then be input into a function , which may generate one or more outputs y. Weight wi applied to an input xi may indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs y, for instance by the corresponding weight having a large numerical value, and / or a “inhibitory,” indicating it has a weak effect influence on the one more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights wimay be determined by training a neural network using training data, which may be performed using any suitable process as described above. Referring now to FIG.9, a flow diagram showing an exemplary planning method 900 is illustrated. In some embodiments, systems and methods described in this disclosure may be used for planning of implantation of a stent. In some embodiments, planning method 900 may include a user feedback module 905 that provides feedback to an operator (user) to ensure that TEE sensor (ultrasound sensor 96) position and orientation has captured frames and views (ultrasound image 94) that are in sync with stent IFU (IFU data 164). Planning method 900 may be implemented by one or more of following components: first component 910, TEE view classification engine and second component 915, TEE angle extraction to capture TEE sensor angle (TEE angle datum 158) rendered on ultrasound frames. In some embodiments, once necessary and sufficient TEE frame views have been captured, the next phase of the planning method 900 may implement a 3D mesh generator 920 that generates a 3D mesh (3D mesh model 132) of heart or coronary artery based on the captured TEE frames. In some embodiments, planning method 900 may be further implemented by one or more of the following components: third component 925, TEE segmentation module, fourth component 930, point cloud completion to generate the 3D mesh and fifth component 935, mesh viewer. In some embodiments, once the 3D mesh is generated, the next phase of the planning method 900 may implement a placement and size determination engine 940 that helps the user determine lock in on a stent size and stent placement (position and orientation), which may be implemented by the following components: sixth component 945, coronary artery / stent-3D visualization including simulation of recommended stent expansion, and the like, seventh component 950, placement simulation engine that tries out many placements and estimates a “placement objective function” and can recommends the top placements (stent datum 140, optimal path 180, and the like) to the user. Referring now to FIG.10, a flow diagram showing an exemplary implantation method 93 Attorney Docket No.1518-194PCT1 1000 is illustrated. In some embodiments, systems and methods described in this disclosure may be used for implantation of a stent. Implantation method 1000 can help a user to execute a recommended placement. In some embodiments, next to a real-time Passthrough Ultrasound viewer (first display window 166a), an Anumana Implantation Helper window (second display window 166b) may render a view of a TEE ultrasound frame (ultrasound image 94) with a recommended stent (stent datum 140) at a recommended placement. The Anumana Implantation Helper window view may be calculated by reading off (using OCR 160) the angle (TEE angle datum 158) displayed in the TEE ultrasound frame and then simulating the TEE frame, with the stent solid model (3D stent model 136) placed at the recommended position. For this calculation, the TEE sensor may be assumed to be at a designated (by stent IFU) position. In some embodiments, implantation method 1000 may be aided by the following components of a virtual TEE guidance module 1005: first component 1010, TEE with stent simulator that generates a TEE frame based on angle and includes the stent solid visualization placed at the recommended placement and second component 1015, TEE view classification engine (view classifier 156) for TEE frame with dynamic catheter and opened stent. Referring now to FIG.11, a flow diagram showing an exemplary post-implantation method 1100 is illustrated. In some embodiments, post-implantation method 1100 may include much of the same functionality as planning method 900, described in this disclosure. Post- implantation method 1100 may also include actual stent placement, overlaid on recommended stent placement. Components that implement post-implantation method 1100 may be same as components described with respect to FIG.8 while the components may be expected to work with the actual stent device at its actual placement. For example, and without limitation, post- implantation method 1100 may be implemented by one or more of following components of following modules: user feedback module 1105 with first component 1110, TEE view classification and second component 1115, TEE angle extraction, 3D mesh generator 1120 with third component 1125, TEE segmentation, fourth component 1130, point cloud completion and fifth component 1135, mesh viewer, and placement and size determination engine 1140 with sixth component 1145, coronary artery / stent-3D visualization and seventh component 1150, placement simulation engine. Referring now to FIG.12, a block diagram of an exemplary system 1200 for transesophageal echocardiogram guided implantation of a valve device is illustrated. System 94 Attorney Docket No.1518-194PCT1 1200 includes at least a transesophageal echocardiogram (TEE) system 1202. In one or more embodiments, TEE system 1202 may include a combination of specialized hardware and software designed to facilitate transesophageal echocardiography by positioning an ultrasound probe (e.g., ultrasound transducer) within esophagus of a patient, close to a heart of the patient. As the esophagus is proximate to the heart, a TEE system 1202 can detect ultrasound image 104 of cardiac tissue of a patient. In one or more embodiments, TEE system 1202 may include a TEE probe (endoscope). The TEE probe is a long, flexible device equipped with an ultrasound transducer (ultrasound sensor 1206) at its tip. Ultrasound transducer can emit high-frequency sound waves and capture the echoes reflected from cardiac structures to produce detailed images (ultrasound image 1204). In a non-limiting example, TEE probe may be inserted into the patient’s esophagus, where the TEE probe may be connected to an ultrasound machine, which processes signals from ultrasound transducer to generate images of the heart. In one or more embodiments, TEE system 1202 may be communicatively connected to at least a display 1208. The display disclosed herein is further described in detail below. Additional disclosure related to TEE system 1202 is further described in detail with respect to FIG.5. With continued reference to FIG.12, TEE system 1202 includes at least an ultrasound sensor 1206 configured to be located within an esophagus of a patient and detect at least an ultrasound image 1204 as a function of cardiac tissue of the patient. In a non-limiting example, ultrasound sensor 1206 may measure the distance to an object using ultrasonic sound waves. Without limitation, sensor may transduce a detected phenomenon, such as without limitation, temperature, voltage, current, pressure, speed, motion, light, moisture, sound waves, and the like, into a sensed signal. Sensor may output the sensed signal. Sensor may include any computing device as described in the entirety of this disclosure and configured to convert and / or translate a plurality of signals detected into electrical signals for further analysis and / or manipulation. Electrical signals may include analog signals, digital signals, periodic or aperiodic signal, step signals, unit impulse signal, unit ramp signal, unit parabolic signal, signum function, exponential signal, rectangular signal, triangular signal, sinusoidal signal, sinc function, or pulse width modulated signal. Any datum captured by sensor may include circuitry, computing devices, electronic components or a combination thereof that translates into at least an electronic signal configured to be transmitted to another electronic component. In a non-limiting embodiment, sensor may include a plurality of sensors included in a sensor suite. In one or more embodiments, 95 Attorney Docket No.1518-194PCT1 and without limitation, sensor may include a plurality of sensors. Sensor may include an ultrasound sensor 1206. With continued reference to FIG.12, in one or more embodiments, ultrasound sensor 1206 may include an electrode. In a non-limiting example, electrode may detect and record electrical activity; for instance, but not limited to, the heart's electrical signals. For example, and without limitation, electrode may generate ultrasonic sound waves, from which ultrasound sensor 1206 receives the ultrasonic waves and transmit ultrasound image 1204 related to the ultrasonic waves to processor 1210. In one or more embodiments, ultrasound sensor 1206 may include a transducer. In a non-limiting example, transducer may operate on a principle of piezoelectricity, where piezoelectric material can convert electrical energy into mechanical vibration (i.e., ultrasonic waves) and vice versa. In one or more embodiments, ultrasound sensor 1206 may include a transceiver. In a non-limiting example, transceiver may transmit ultrasonic waves and receive echoes. With continued reference to FIG.12, in a non-limiting example, ultrasound image 1204 may include visual representation of a heart examined through esophagus. As a non-limiting example, ultrasound image 1204 may include distance between sensor and surrounding tissue or organs. In one or more embodiments, ultrasound sensor 1206 may detect ultrasound image 1204 in a plurality of angles. In a non-limiting example, ultrasound image 1204 may include a plurality of distances between sensor and a heart in different angles. For example, and without limitation, when ultrasound sensor 1206 moves around within an esophagus, ultrasound sensor 1206 receives a plurality of distances between ultrasound sensor 1206 and organ and generate ultrasound image 1204 using the plurality of distances. With continued reference to FIG.12, in one or more embodiments, ultrasound sensor may be configured to capture an ultrasound image 1204 of a cardiac tissue of a patient. “Cardiac tissue” as described in this disclosure refers to any portion of an individual’s heart. For example and without limitation, cardiac tissue may include heart valves, chambers, protective sacs and / or the like. In a non-limiting example, ultrasound image 1204 may include an image of a heart before implantation of a cardiovascular device, during implantation of a cardiovascular device and / or after implantation of a cardiovascular device. For example, and without limitation, ultrasound image 1204 may include an image of a valve of a heart that may not be working properly. In one or more embodiments, ultrasound sensor may be configured to capture the 96 Attorney Docket No.1518-194PCT1 internal surroundings of a patient’s heart. With continued reference to FIG.12, system 1200 includes a computing device 1212. Computing device 1212 includes a processor 1210 communicatively connected to a memory 1214. Without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and / or transmittance of data and / or signal(s) therebetween. Data and / or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and / or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device 1212. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. With continued reference to FIG.12, in one or more embodiments, computing device 1212 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and / or system on a chip (SoC) as described in this disclosure. Computing device 1212 may include, be included in, and / or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 1212 may include a single computing device operating independently, or may include two or more computing devices operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Computing device 1212 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device 1212 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not 97 Attorney Docket No.1518-194PCT1 limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and / or from a computer and / or a computing device 1212. Computing device 1212 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Computing device 1212 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 1212 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Computing device 1212 may be implemented, as a non- limiting example, using a “shared nothing” architecture. With continued reference to FIG.12, in one or more embodiments, computing device 1212 may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device 1212 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device 1212 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in 98 Attorney Docket No.1518-194PCT1 which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing. With continued reference to FIG.12, memory 1214 contains instructions configuring processor 1210 to receive at least an ultrasound image 1204. In one or more embodiments, processor 1210 may receive ultrasound image 1204 from TEE system 1202. In one or more embodiments, processor 1210 may receive ultrasound image 1204 from a cardio database 1216. In one or more embodiments, system 1200 may include a Cardio database 1216. As used in this disclosure “cardio database” is a data structure configured to store data associated with one or more patient’s hearts. In one or more embodiments, Cardio database 1216 may include inputted or calculated information and datum related to a patient’; s heart such as chambers, blood vessels and / or the like. In one or more embodiments, a datum history may be stored in Cardio database 1216. As a non-limiting example, the datum history may include real-time and / or previous inputted data related to the patient’s heart. As a non-limiting example, Cardio database 1216 may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, where the instructions may include examples of the data related to a patient’s heart. With continued reference to FIG.12, in one or more embodiments, processor 1210 may be communicatively connected with Cardio database 1216. For example, and without limitation, in one or more embodiments, Cardio database 1216 may be local to processor 1210. In another example, and without limitation, Cardio database 1216 may be remote to processor 1210 and communicative with processor 1210 by way of one or more networks. The network may include, but is not limited to, a cloud network, a mesh network, and the like. The network may use an immutable sequential listing to securely store Cardio database 1216. An immutable sequential listing may be, include and / or implement an immutable ledger, where data entries that have been posted to the immutable sequential listing cannot be altered. With continued reference to FIG.12, in one or more embodiments, Cardio database 1216 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and / or data structure, such as a distributed hash table or the like. Database may include 99 Attorney Docket No.1518-194PCT1 a plurality of data entries and / or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and / or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and / or reflect data and / or records as used herein, as well as categories and / or populations of data consistently with this disclosure. With continued reference to FIG.12, in one or more embodiments, processor 1210 may receive ultrasound image 1204 from a user device 1218. As a non-limiting example, a user may include a surgeon, doctor, medical professional, and the like. As a non-limiting example, user device 1218 may include a laptop, desktop, tablet, mobile phone, smart phone, smart watch, kiosk, screen, smart headset, or things of the like. In one or more embodiments, user device 1218 may include an interface configured to receive inputs from user. In one or more embodiments, user may manually input any data into computing device 1212 using user device 1218. In one or more embodiments, user may have a capability to process, store or transmit any information independently. With continued reference to FIG.12, in one or more embodiments, receiving at least an ultrasound image 1204 may include extracting at least a TEE angle datum 1220 from at least an ultrasound image 1204 using an optical character recognition. In one or more embodiments, TEE angle datum 1220 may be angular orientation of an ultrasound sensor 1206 relative to a reference axis or plane (e.g., the anatomical position of a heart). As a non-limiting example, TEE angle datum 1220 may include TEE probe's imaging angle, such as 0°, 45°, 90°, or 135°, which may determine the plane of the ultrasound slice captured during imaging. With continued reference to FIG.12, ultrasound image 1204 may be received through a TEE procedure. In one or more embodiments, ultrasound image may be received from TEE system 1202 and / or ultrasound sensor 1206 during a TEE procedure. TEE procedure is described in further detail below in reference to at least FIG.13. In one or more embodiments, ultrasound image may include TEE echocardiogram data as described in reference to at least FIG.13. With continued reference to FIG.12, in one or more embodiments, processor 1210 may analyze ultrasound image 1204 to find TEE angle datum 1220 using optical character recognition (OCR) 1222. In one or more embodiments, ultrasound image 1204 may include a plurality of 100 Attorney Docket No.1518-194PCT1 words related to position and orientation of ultrasound sensor 1206 within esophagus. In one or more embodiments, the at least a processor 1210 may be configured to recognize a keyword using the OCR 1222 to find the TEE angle datum 1220. In one or more embodiments, the at least a processor 1210 may transcribe much or even substantially all ultrasound images 104. With continued reference to FIG.12, in one or more embodiments, optical character recognition 1222 or optical character reader (OCR) may include automatic conversion of images of written (e.g., typed, handwritten or printed text) into machine-encoded text. In one or more embodiments, recognition of a keyword from ultrasound image 1204 may include one or more processes, including without limitation optical character recognition (OCR) 1222, optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In one or more embodiments, OCR 1222 may recognize written text, one glyph or character at a time. In one or more embodiments, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In one or more embodiments, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. In one or more embodiments, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes. With continued reference to FIG.12, in one or more embodiments, OCR 1222 may be an "offline" process, which analyses a static document or image frame. In one or more embodiments, handwriting movement analysis can be used as input to handwriting recognition. For example, instead of merely using shapes of glyphs and words, this technique may capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information may make handwriting recognition more accurate. In one or more embodiments, this technology may be referred to as “online” character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition. With continued reference to FIG.12, in one or more embodiments, OCR processes may employ pre-processing of ultrasound image 1204. Pre-processing process may include without limitation de-skew, de-speckle, binarization, line removal, layout analysis or “zoning,” line and word detection, script recognition, character isolation or “segmentation,” and normalization. In one or more embodiments, a de-skew process may include applying a transform (e.g., 101 Attorney Docket No.1518-194PCT1 homography or affine transform) to the ultrasound image 1204 to align text. In one or more embodiments, a de-speckle process may include removing positive and negative spots and / or smoothing edges. In one or more embodiments, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image). Binarization may be performed as a simple way of separating text (or any other desired image component) from a background of image component. In one or more embodiments, binarization may be required for example if an employed OCR algorithm only works on binary images. In one or more embodiments, a line removal process may include removal of non-glyph or non-character imagery (e.g., boxes and lines). In one or more embodiments, a layout analysis or “zoning” process may identify columns, paragraphs, captions, and the like as distinct blocks. In one or more embodiments, a line and word detection process may establish a baseline for word and character shapes and separate words, if necessary. In one or more embodiments, a script recognition process may, for example, in multilingual documents, identify script allowing an appropriate OCR algorithm to be selected. In one or more embodiments, a character isolation or “segmentation” process may separate signal characters, for example character-based OCR algorithms. In one or more embodiments, a normalization process may normalize aspect ratio and / or scale of image component. With continued reference to FIG.12, in one or more embodiments an OCR process may include an OCR algorithm. Exemplary OCR algorithms include matrix matching process and / or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by-pixel basis. In some case, matrix matching may also be known as “pattern matching,” “pattern recognition,” and / or “image correlation.” Matrix matching may rely on an input glyph being correctly isolated from the rest of the image component. Matrix matching may also rely on a stored glyph being in a similar font and at a same scale as input glyph. Matrix matching may work best with typewritten text. With continued reference to FIG.12, in one or more embodiments, an OCR process may include a feature extraction process. In one or more embodiments, feature extraction may decompose a glyph into a feature. Exemplary non-limiting features may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In one or more embodiments, feature extraction may reduce dimensionality of representation and may make the recognition process computationally more efficient. In one or more embodiments, extracted feature may be 102 Attorney Docket No.1518-194PCT1 compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR 1222. In one or more embodiments, machine-learning processes like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) may be used to compare image features with stored glyph features and choose a nearest match. OCR 1222 may employ any machine- learning process described in this disclosure, for example machine-learning processes described with reference to FIG.6. Exemplary non-limiting OCR software may include Cuneiform and Tesseract. Cuneiform may include a multi-language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract may include free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States. With continued reference to FIG.12, in one or more embodiments, OCR 1222 may employ a two-pass approach to character recognition. A first pass may try to recognize a character. Each character that is satisfactory may be passed to an adaptive classifier as training data. The adaptive classifier then may get a chance to recognize characters more accurately as it further analyzes ultrasound image 1204. Since the adaptive classifier may have learned something useful a little too late to recognize characters on the first pass, a second pass may be run over the ultrasound image 1204. Second pass may include adaptive recognition and use characters recognized with high confidence on the first pass to recognize better remaining characters on the second pass. In one or more embodiments, two-pass approach may be advantageous for unusual fonts or low-quality image components where visual verbal content may be distorted. Another exemplary OCR software tool may include OCRopus. OCRopus development is led by German Research Centre for Artificial Intelligence in Kaiserslautern, Germany. In one or more embodiments, OCR software may employ neural networks. With continued reference to FIG.12, in one or more embodiments, OCR 1222 may include post-processing. For example, OCR accuracy may be increased, in one or more embodiments, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In one or more embodiments, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In one or more embodiments, an output stream may be a plain text stream or file of characters. In one or more embodiments, an OCR process may preserve an original layout of visual verbal 103 Attorney Docket No.1518-194PCT1 content. In one or more embodiments, near-neighbor analysis can make use of co-occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In one or more embodiments, an OCR process may make us of a priori knowledge of grammar for a language being recognized. For example, grammar rules may be used to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post- processing to further optimize results. With continued reference to FIG.12, in one or more embodiments, processor 1210 may generate an image inquiry datum 1224 as a function of the at least a TEE angle datum 1220 and device instruction for use (IFU) data 1226 retrieved from Cardio database 1216. In one or more embodiments, image inquiry datum 1224 may include a control signal that is transmitted to TEE system 1202 to obtain necessary additional ultrasound images 104. In a non-limiting example, if processor 1210 determines that ultrasound images in specific views or specific angles and / or a number of ultrasound images are missing based on device IFU data 1226, processor 1210 may request additional ultrasound images 104 from TEE system 1202. In one or more embodiments, image inquiry datum 1224 may include a notification for a user. In a non-limiting example, if processor 1210 determines that ultrasound images in specific views or specific angles and / or a number of ultrasound images are missing based on device IFU data 1226, processor 1210 may generate a notification and may transmit to a user to operate TEE system 1202 to generate additional ultrasound images 104 more and / or in the specific views or specific angles. In a non- limiting example, image inquiry datum 1224 may be configured to query additional ultrasound images 104 to a user of at least a TEE system 1202 through at least a display 1208. With continued reference to FIG.12, for the purposes of this disclosure, “device Instructions for Use (IFU) data” is data related to procedural guidelines or specifications associated with cardiovascular device. As a non-limiting example, device IFU data 1226 may include usage instructions, imaging requirements, and the like. For example, and without limitation, device IFU data 1226 may include specific views or angles of ultrasound images 104 necessary for a placement of cardiovascular device on a coronary artery. A “cardiovascular device” as described in this disclosure refers to a medical device that is used in the management of diseases or conditions affecting the heart. For example, and without limitation, cardiovascular 104 Attorney Docket No.1518-194PCT1 device may include heart valves. In one or more embodiments, cardiovascular device may be used for heart valve repair as described in this disclosure. In one or more embodiments, cardiovascular device may be used to stabilize and reshape the ring around heart valves to restore normal valve function. In one or more embodiments, cardiovascular device may be used to reinforce the areas of a heart valve that may be weakened or damaged. In one or more embodiments, cardiovascular device may be implanted within the heart of a patient in order to improve the functioning of one or more arteries and / or valves. In one or more embodiments, cardiovascular device may include a stent. A “stent” as described in this disclosure is a tube configured to be implanted within the heart of a patient and used to keep the arteries of veins open. In one or more embodiments, stent may include a bare-metal stent, a drug-eluding stent, a biodegradable stent, a covered stent, a graft stent, a self-expanding stent, a ballon-expandable sent and / or the like. In one or more embodiments, cardiovascular device may include a heart valve. A “heart valve” or “valve” as described int this disclosure is prosthetic calve that is configured to replace or repair a damaged heart valve. In one or more embodiments, heart valve may include heart valves, such as but not limited to, tilting disc valves, stented tissue valves, stent less tissue valves, homograft, autografts, transcatheter heart valves, aortic valves, mitral valves, tricuspid valves, pulmonary valves and / or the like. In one or more embodiments, IFU data 1226 may include specific view of angles needed for a medical professional to properly implant cardiovascular device within a patient. In one or more embodiments, device IFU data 1226 may be stored in a Cardio database 1216. In one or more embodiments, processor 1210 may retrieve device IFU data 1226 from Cardio database 1216 as a function of valve model datum 1228. As a non-limiting example, processor may retrieve device IFU data 1226 that is related to a cardiovascular device selected specifically for a patient. In one or more embodiments, cardiovascular device may be used to keep arteries or veins open. In one or more embodiments, cardiovascular device may include prosthetic valves that replace or repair damaged valves. With continued reference to FIG.12, in one or more embodiments, receiving at least an ultrasound image 1204 may include generating view training data 1232, wherein the view training data 1232 may include exemplary ultrasound images correlated to exemplary view labels, training a view classifier 1234 using the view training data 1232, classifying the at least an ultrasound image 1204 to at least a view label 1236 using the trained view classifier 1234 and 105 Attorney Docket No.1518-194PCT1 generating an image inquiry datum 1224 as a function of the view label 1236 and device IFU data 1226. As a non-limiting example, view label 1236 may include mid-esophageal four- chamber view, mid-esophageal bicaval view, mid-esophageal long-axis view, mid-esophageal left atrial appendage view, transgastric short-axis view, transgastric two-chamber view, aortic valve short-axis view, and the like. In one or more embodiments, view label 1236 may be retrieved from Cardio database 1216. In one or more embodiments, user may manually input view label 1236 of ultrasound images 1204. With continued reference to FIG.12, in one or more embodiments, view training data 1232 may be stored in Cardio database 1216. In one or more embodiments, view training data 1232 may be received from one or more users, Cardio database 1216, external computing devices, and / or previous iterations of processing. As a non-limiting example, view training data 1232 may include instructions from a user, who may be an expert user, a past user in embodiments disclosed herein, or the like, which may be stored in memory and / or stored in Cardio database 1216, where the instructions may include labeling of training examples. In one or more embodiments, view training data 1232 may be updated iteratively on a feedback loop. As a non-limiting example, processor 1210 may update view training data 1232 iteratively through a feedback loop as a function of ultrasound image 1204, TEE angle datum 1220, view label 1236, device IFU data 1226, or the like. In one or more embodiments, processor 1210 may be configured to generate a view classifier 1234. In a non-limiting example, generating view classifier 1234 may include training, retraining, or fine-tuning view classifier 1234 using view training data 1232 or updated view training data 1232. In one or more embodiments, processor 1210 may be configured to determine view label 1236 using view classifier 1234 (i.e., trained or updated view classifier 1234). In one or more embodiments, generating training data and training machine-learning models may be simultaneous. With continued reference to FIG.12, memory 1214 contains instructions configuring processor 1210 to generate at least a 3D cardiac model representative of a patient’s heart as a function of at least an ultrasound image 1204. In some embodiments, 3D cardiac model may include peripheral vasculature. In a non-limiting example, 3D cardiac model may include a 3D voxel occupancy representation (VOR). While a pixel represents a point in a 2D image and may include properties such as color and / or brightness, a voxel may represent a volume in a 3D space and may include additional properties such density / occupancy as described below. In an 106 Attorney Docket No.1518-194PCT1 embodiment, each voxel of plurality of voxels within 3D VOR may represent a specific portion of heart. In one or more embodiments, voxel may be a smallest distinguishable box-shaped part (i.e., 1px·1px·1px) of a three-dimensional image. In one or more embodiments, each voxel of plurality of voxels within VOR may be represented as a cube or rectangular prism (although other shapes may be used in specialized applications). Each voxel may include a size that determines a resolution of the 3D image or model. In an embodiment, smaller voxels may provide higher resolution; however, it may require more computational resources (e.g., RAM) for processor 1210 to process. In an embodiment, and still referring to FIG.12, each voxel of plurality of voxels within VOR may include one or more embedded values. In one or more embodiments, embedded values may represent various attributes or characteristics of the corresponding portion of heart that voxel represents. In a non-limiting example, embedded values may include density values, intensity values, texture information, or any other quantitative measures that provide insights into the underlying cardiac tissue. Such embedded values may be derived from set of ultrasonic images or other imaging modalities used to generate 3D cardiac model. In one or more embodiments, embedded values may be utilized, by processor 1210, to differentiate between different types of cardiac tissues, such as myocardial tissue, blood vessels, or chambers. Embedded values may also facilitate the visualization of dynamic cardiac functions, for example, and without limitation, blood flow or heart beating by encoding temporal information such as timestamps within plurality of voxels. In one or more embodiments, and still reference to FIG.12, one or more embedded values, such as, without limitations, occupancy, or density, may be derived from ultrasound images 1204 described herein by processor 1210. In a non-limiting example, determining occupancy status of each voxel of plurality of voxels may include converting set of ultrasonic images 1204 to a set of binary images and determining occupancy status of each voxel as a function of the structure of interest’s binary value. In one or more embodiments, occupancy status may include a value representing the likelihood of occupancy of the corresponding heart tissue. In another non-limiting example, density may be calculated, by processor 1210, for each voxel as a function of the echogenicity of one or more pixels on a given ultrasound image 1204, wherein, the brightness of the given ultrasonic image may be analyzed since different tissues reflect ultrasound waves differently. 107 Attorney Docket No.1518-194PCT1 With continued reference to FIG.12, generating 3D cardiac model of heart may include generating a 3D array. In one or more embodiments, processor 1210 may divide 3D space into a grid of plurality of voxels, each with specific x, y, and z coordinates as embedded values. Each element of 3D array may correspond to a voxel. In one or more embodiments, 3D array may allow for easy access and manipulation of plurality of voxels, enabling various analyses, visualizations, and transformations either described or not described herein. In a non-limiting example, embedded values may include a density of the tissue at a specific location of a patient’s body derived from one or more ultrasonic images of ultrasound images 1204. Additionally, or alternatively, and still referring to FIG.12, 3D cardiac model of heart may include a 3D grid embedded values described herein of plurality of voxels (e.g., tissue density, blood flow velocity, echogenicity or acoustic properties, and any other biophysical properties). In an embodiment, each cell within 3D grid may be associated with a distinct voxel. In yet another embodiment, and...

Claims

1. WHAT IS CLAIMED IS:

1. A system for transesophageal echocardiogram-guided implantation of a stent, the system comprising: at least a transesophageal echocardiogram (TEE) system comprising at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect a plurality of ultrasound images as a function of cardiac tissue of the patient; at least a display; and at least a computing device comprising at least a processor and a memory containing instructions configuring the at least a processor to: receive the plurality of ultrasound images; generate at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images; receive at least a 3D stent model representative of a stent; determine a view label for each of the plurality of ultrasound images; and display, using the at least a display, at least a portion of the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises superimposing the at least a 3D stent model onto the at least a 3D cardiac model.

2. The system of claim 1, wherein generating the 3D cardiac model comprises generating the 3D cardiac model using a statistical shape model.

3. The system of claim 1, wherein receiving the at least a 3D stent model comprises: determining a stent datum as a function of at least a cardiac featuring datum and patient data; and generating the at least a 3D stent model as a function of the stent datum.

4. The system of claim 1, wherein determining the view label comprises extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition.

5. The system of claim 1, wherein determining the view label comprises: generating view training data, wherein the view training data comprises exemplary 148 Attorney Docket No.1518-194PCT1 ultrasound images correlated to exemplary view labels; training a view classifier using the view training data; and determining the view label for each of the plurality of ultrasound images using the trained view classifier.

6. The system of claim 1, wherein displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model comprises: generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label; and superimposing the at least a 3D stent model on to the pseudo TEE frame.

7. The system of claim 1, wherein superimposing the at least a 3D stent model comprises: determining a position datum at the at least a 3D cardiac model as a function of a density datum of at least a cardiac featuring datum; and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum.

8. The system of claim 7, wherein determining the position datum comprises determining the position datum as a function of a user input received from a user interface presented on the at least a display.

9. The system of claim 7, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises generating a notification datum as a function of the position datum and the at least a 3D stent model.

10. The system of claim 1, wherein superimposing the at least a 3D stent model onto the at least a 3D cardiac model comprises: determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model; generating a path model for the optimal path; and superimposing the path model onto the at least a 3D cardiac model.

11. The system of claim 1, wherein the 3D cardiac model comprises peripheral vasculature.

12. The system of claim 1, wherein generating the at least a 3D cardiac model comprises generating the at least a 3D cardiac model using a point completion model.

13. A method of transesophageal echocardiogram-guided implantation of a stent, the method comprising: 149 Attorney Docket No.1518-194PCT1 receiving, using at least a processor, a plurality of ultrasound images from at least a transesophageal echocardiogram (TEE) system comprising at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect the plurality of ultrasound images as a function of cardiac tissue of the patient; generating, using the at least a processor, at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images; receiving, using the at least a processor, at least a 3D stent model representative of a stent; determining, using the at least a processor, a view label for each of the plurality of ultrasound images; and displaying, using the at least a processor and at least a display, the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises superimposing the at least a 3D stent model onto the at least a 3D cardiac model.

14. The method of claim 13, wherein generating the 3D cardiac model comprises generating the 3D cardiac model using a statistical shape model.

15. The method of claim 13, wherein receiving the at least a 3D stent model comprises: determining a stent datum as a function of at least a cardiac featuring datum and patient data; and generating the at least a 3D stent model as a function of the stent datum.

16. The method of claim 13, wherein determining the view label comprises extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition.

17. The method of claim 13, wherein determining the view label comprises: generating view training data, wherein the view training data comprises exemplary ultrasound images correlated to exemplary view labels; training a view classifier using the view training data; and determining the view label for each of the plurality of ultrasound images using the trained 150 Attorney Docket No.1518-194PCT1 view classifier.

18. The method of claim 13, wherein displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model comprises: generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label; and superimposing the at least a 3D stent model on to the pseudo TEE frame.

19. The method of claim 13, wherein superimposing the at least a 3D stent model comprises: determining a position datum at the at least a 3D cardiac model as a function of a density datum of at least a cardiac featuring datum; and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum.

20. The method of claim 19, wherein determining the position datum comprises determining the position datum as a function of a user input received from a user interface presented on the at least a display.

21. The method of claim 19, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises generating a notification datum as a function of the position datum and the at least a 3D stent model.

22. The method of claim 13, wherein superimposing the at least a 3D stent model onto the at least a 3D cardiac model comprises: determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model; generating a path model for the optimal path; and superimposing the path model onto the at least a 3D cardiac model.

23. The method of claim 13, wherein the 3D cardiac model comprises peripheral vasculature.

24. The method of claim 13, wherein generating the at least a 3D cardiac model comprises generating the at least a 3D cardiac model using a point completion model.

25. A system for transesophageal echocardiogram-guided implantation of a stent, the system comprising: at least a transesophageal echocardiogram (TEE) system comprising at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within an esophagus of a patient and detect a plurality of ultrasound 151 Attorney Docket No.1518-194PCT1 images as a function of cardiac tissue of the patient; at least a display; and at least a computing device comprising at least a processor and a memory containing instructions configuring the at least a processor to: receive the plurality of ultrasound images; generate at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images by using a point completion model, wherein: the point completion model uses a view-guided approach including a view-guided framework that retrieves absent global shape information of the heart from alternative single-view images for point cloud completion; receive at least a 3D stent model representative of a stent; determine a view label for each of the plurality of ultrasound images; and display, using the at least a display, at least a portion of the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises superimposing the at least a 3D stent model onto the at least a 3D cardiac model.

26. The system of claim 25, wherein generating the 3D cardiac model comprises generating the 3D cardiac model using a statistical shape model.

27. The system of claim 25, wherein receiving the at least a 3D stent model comprises: determining a stent datum as a function of at least a cardiac featuring datum and patient data; and generating the at least a 3D stent model as a function of the stent datum.

28. The system of claim 25, wherein determining the view label comprises extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition.

29. The system of claim 25, wherein determining the view label comprises: generating view training data, wherein the view training data comprises exemplary ultrasound images correlated to exemplary view labels; 152 Attorney Docket No.1518-194PCT1 training a view classifier using the view training data; and determining the view label for each of the plurality of ultrasound images using the trained view classifier.

30. The system of claim 25, wherein displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model comprises: generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label; and superimposing the at least a 3D stent model on to the pseudo TEE frame.

31. The system of claim 25, wherein superimposing the at least a 3D stent model comprises: determining a position datum at the at least a 3D cardiac model as a function of a density datum of at least a cardiac featuring datum; and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum.

32. The system of claim 31, wherein determining the position datum comprises determining the position datum as a function of a user input received from a user interface presented on the at least a display.

33. The system of claim 31, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises generating a notification datum as a function of the position datum and the at least a 3D stent model.

34. The system of claim 25, wherein superimposing the at least a 3D stent model onto the at least a 3D cardiac model comprises: determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model; generating a path model for the optimal path; and superimposing the path model onto the at least a 3D cardiac model.

35. The system of claim 25, wherein the 3D cardiac model comprises peripheral vasculature.

36. A method of transesophageal echocardiogram-guided implantation of a stent, the method comprising: receiving, using at least a processor, a plurality of ultrasound images from at least a transesophageal echocardiogram (TEE) system comprising at least an ultrasound sensor, wherein the at least an ultrasound sensor is configured to be located within 153 Attorney Docket No.1518-194PCT1 an esophagus of a patient and detect the plurality of ultrasound images as a function of cardiac tissue of the patient; generating, using the at least a processor, at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the plurality of ultrasound images by using a point completion model, wherein: the point completion model uses a view-guided approach including a view-guided framework that retrieves absent global shape information of the heart from alternative single-view images for point cloud completion; receiving, using the at least a processor, at least a 3D stent model representative of a stent; determining, using the at least a processor, a view label for each of the plurality of ultrasound images; and displaying, using the at least a processor and at least a display, the at least a 3D cardiac model and the at least a 3D stent model as a function of the view label, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises superimposing the at least a 3D stent model onto the at least a 3D cardiac model.

37. The method of claim 36, wherein generating the 3D cardiac model comprises generating the 3D cardiac model using a statistical shape model.

38. The method of claim 36, wherein receiving the at least a 3D stent model comprises: determining a stent datum as a function of at least a cardiac featuring datum and patient data; and generating the at least a 3D stent model as a function of the stent datum.

39. The method of claim 36, wherein determining the view label comprises extracting an TEE angle datum from the plurality of ultrasound images using an optical character recognition.

40. The method of claim 36, wherein determining the view label comprises: generating view training data, wherein the view training data comprises exemplary ultrasound images correlated to exemplary view labels; training a view classifier using the view training data; and determining the view label for each of the plurality of ultrasound images using the trained 154 Attorney Docket No.1518-194PCT1 view classifier.

41. The method of claim 36, wherein displaying the at least a portion of the at least a 3D cardiac model and the at least a 3D stent model comprises: generating a pseudo TEE frame as a function of the at least a 3D cardiac model and the view label; and superimposing the at least a 3D stent model on to the pseudo TEE frame.

42. The method of claim 36, wherein superimposing the at least a 3D stent model comprises: determining a position datum at the at least a 3D cardiac model as a function of a density datum of at least a cardiac featuring datum; and superimposing the at least a 3D stent model onto the at least a 3D cardiac model as a function of the position datum.

43. The method of claim 42, wherein determining the position datum comprises determining the position datum as a function of a user input received from a user interface presented on the at least a display.

44. The method of claim 42, wherein displaying the at least a 3D cardiac model and the at least a 3D stent model comprises generating a notification datum as a function of the position datum and the at least a 3D stent model.

45. The method of claim 36, wherein superimposing the at least a 3D stent model onto the at least a 3D cardiac model comprises: determining an optimal path for a placement of the at least a 3D stent model within the at least a 3D cardiac model; generating a path model for the optimal path; and superimposing the path model onto the at least a 3D cardiac model.

46. The method of claim 36, wherein the 3D cardiac model comprises peripheral vasculature.

47. A system for transesophageal echocardiogram-guided implantation of a valve device, the system comprising: at least a transesophageal echocardiogram system comprising at least an ultrasound sensor configured to be located within an esophagus of a patient and detect at least an ultrasound image as a function of cardiac tissue of the patient; and at least a computing device configured to: receive the at least an ultrasound image; 155 Attorney Docket No.1518-194PCT1 generate at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the at least an ultrasound image wherein the at least an ultrasound image comprises a two-dimensional image of the heart of the patient; determine a valve model datum as a function of the 3D cardiac model; receive at least a valve model representative of at least a cardiovascular device to be placed within the patient as a function of the valve model datum; generate at least a recommended cardiovascular device placement; and display the at least a 3D cardiac model and the at least a valve model, wherein displaying the at least a 3D cardiac model and the at least a valve model comprises displaying an actual cardiovascular device placement overlaid on the recommended cardiovascular device placement.

48. The system of claim 47, wherein displaying the at least a 3D cardiac model and the at least a valve model comprises: generating a pseudo transesophageal echocardiogram (TEE) frame as a function of the at least a 3D cardiac model and a view label; and superimposing the at least a valve model on to the pseudo TEE frame.

49. The system of claim 47, wherein the valve model datum comprises information associated with a dimension of the cardiovascular device.

50. The system of claim 47, wherein displaying the at least a 3D cardiac model and the at least valve model comprises: superimposing the at least a valve model onto to the 3D cardiac model to create a superimposed model; and displaying the superimposed model.

51. The system of claim 50, wherein displaying the superimposed model further comprises displaying a path model for implantation of the cardiovascular device within the heart of the patient.

52. The system of claim 47, wherein generating the at least a 3D cardiac model comprises: extracting at least a cardiac feature from the at least an ultrasound image; and segmenting the at least an ultrasound image into a plurality of image segments as a function of the at least a cardiac feature. 156 Attorney Docket No.1518-194PCT153. The system of claim 47, wherein generating the at least a 3D cardiac model representative of the heart of the patient comprises: receiving a generic 3D model; identifying one or more anomalies within the at least an ultrasound image; and generating the at least a 3D cardiac model as a function of the generic 3D model and the one or more anomalies using a statistical shape model.

54. The system of claim 53, wherein at least one anomaly of the one or more anomalies comprises a spatial distortion of at least one cardiac feature.

55. The system of claim 47, wherein receiving the at least valve model representative of the at least a cardiovascular device to be placed within the patient comprises: generating a search query for a device database as a function of the valve model datum, wherein the device database comprises a plurality of valve models representative of a plurality of cardiovascular devices; and identifying at least one available valve model from the plurality of valve models as a function of the search query.

56. The system of claim 55, wherein the search query comprises an annulus diameter associated with the valve model.

57. The system of claim 47, wherein generating the at least a 3D cardiac model comprises generating the at least a 3D cardiac model using a point completion model.

58. A method of transesophageal echocardiogram-guided implantation of a valve device, the method comprising: detecting, by at least a transesophageal echocardiogram system, at least an ultrasound image, wherein the at least a transesophageal echocardiogram system comprises at least an ultrasound sensor configured to be located within an esophagus of a patient and detect the at least an ultrasound image as a function of cardiac tissue of the patient; receiving, by at least a computing device, the at least an ultrasound image; generating, by the at least a computing device, at least a three-dimensional (3D) cardiac model representative of a heart of the patient as a function of the at least an ultrasound image wherein the at least an ultrasound image comprises a two- dimensional image of the heart of the patient; 157 Attorney Docket No.1518-194PCT1determining, by the at least a computing device, a valve model datum as a function of the 3D cardiac model; receiving, by the at least a computing device, at least a valve model representative of at least a cardiovascular device to be placed within the patient as a function of the valve model datum; generating at least a recommended cardiovascular device placement; and displaying, by the at least a computing device, the at least a 3D cardiac model and the at least a valve model, wherein displaying the at least a 3D cardiac model and the at least a valve model comprises displaying an actual cardiovascular device placement overlaid on the recommended cardiovascular device placement.

59. The method of claim 58, wherein displaying, by the at least a computing device, the at least a 3D cardiac model and the at least a valve model comprises: generating a pseudo transesophageal echocardiogram (TEE) frame as a function of the at least a 3D cardiac model and a view label; and superimposing the at least a valve model on to the pseudo TEE frame.

60. The method of claim 58, wherein the valve model datum comprises information associated with a dimension of the cardiovascular device.

61. The method of claim 58, wherein displaying, by the at least a computing device, the at least a 3D cardiac model and the at least valve model comprises: superimposing the at least a valve model onto to the 3D cardiac model to create a superimposed model; and displaying the superimposed model.

62. The method of claim 61, wherein displaying the superimposed model further comprises displaying a path model for implantation of the cardiovascular device within the heart of the patient.

63. The method of claim 58, wherein generating, by the at least a computing device, the at least a 3D cardiac model comprises: extracting at least a cardiac feature from the at least an ultrasound image; and segmenting the at least an ultrasound image into a plurality of image segments as a function of the at least a cardiac feature.

64. The method of claim 58, wherein generating, by the at least a computing device, the at 158 Attorney Docket No.1518-194PCT1least a 3D cardiac model representative of the heart of the patient comprises: receiving a generic 3D model; identifying one or more anomalies within the at least an ultrasound image; and generating the at least a 3D cardiac model as a function of the generic 3D model and the one or more anomalies using a statistical shape model.

65. The method of claim 64, wherein at least one anomaly of the one or more anomalies comprises a spatial distortion of at least one cardiac feature.

66. The method of claim 58, wherein receiving, by the at least a computing device, the at least valve model representative of the at least a cardiovascular device to be placed within the patient comprises: generating a search query for a device database as a function of the valve model datum, wherein the device database comprises a plurality of valve models representative of a plurality of cardiovascular devices; and identifying at least one available valve model from the plurality of valve models as a function of the search query.

67. The method of claim 66, wherein the search query comprises an annulus diameter associated with the valve model.

68. The method of claim 58, wherein generating the at least a 3D cardiac model comprises generating the at least a 3D cardiac model using a point completion model. 159 Attorney Docket No.1518-194PCT1

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