Apparatus and methods for generating electro-anatomical mapping

Abstract

An exemplary apparatus according to this disclosure includes at least a display, a processor, and a memory communicatively connected to the processor. The processor generates a display data structure comprising a plurality of visual elements, configures a display device to display the visual elements, receives an activation of an event handler of the plurality of event handlers, wherein receiving the activation further comprises receiving a user selection of a visual element of the plurality of visual elements that is linked to the event handler, and executes the algorithm module associated with the activated event handler comprising an image processing module configured to receive an initial image and output a modified image.

Description

[0001]APPARATUS AND METHODS FOR GENERATING ELECTRO-ANATOMICAL MAPPING CROSS-REFERENCE TO RELATED APPLICATIONS This application is an International PCT application, and claims priority to U.S. Patent Application No.19 / 068,248, filed on March 3, 2025, entitled “APPARATUS AND METHODS FOR GENERATING ELECTRO-ANATOMICAL MAPPING,” and U.S. Non-Provisional Patent Application No.18 / 788,566, filed on July 30, 2024, now U.S. Patent No.12,266,450 issued on April 1, 2025, entitled “APPARATUS AND METHODS FOR GENERATING ELECTRO-ANATOMICAL MAPPING,” the entirety of each of which is incorporated herein by reference. FIELD OF THE INVENTION The present invention generally relates to the field of medical imaging and clinical decision support. In particular, the present invention is directed to apparatus and methods for generating electro-anatomical mapping. BACKGROUND Electro-anatomical mapping is a sophisticated diagnostic tool that merges electrical activity data with anatomical imaging to create a detailed, 3D representation of the conduction system within an organ or a tissue. It is often used to accurately diagnose and treat cardiac arrhythmias by providing clinicians with precise information about the heart’s electrical pathways and structural features. However, traditional electro-anatomical mapping techniques also require specialized medical facilities such as Electrophysiology Labs. Additionally, these techniques often involve invasive and potentially harmful procedures such as insertion of catheters and exposure to X-rays. As a result, patients undergoing such procedures often need to endure high costs as well as significant risk and discomfort. SUMMARY OF THE DISCLOSURE In some aspects, the techniques described herein relate to an apparatus for generating electro-anatomical mapping, wherein the apparatus includes: a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to: receive input data, wherein receiving the input data includes: receiving, from an imaging device, at least a medical image and receiving, from a signal 1 Attorney Docket No.1518-164PCT1 capturing device, at least an electrogram, generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, wherein the at least a machine learning model is trained using electro-anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output, and display the electro-anatomical mapping using a user interface, wherein generating the electro-anatomical mapping includes: generating a putative electro-anatomical mapping, validating the putative electro-anatomical mapping using a plurality of quality assurance parameters, and creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation. In some aspects, the techniques described herein relate to an apparatus, wherein the at least a medical image includes one or more of at least a computed tomography (CT) scan and at least a magnetic resonance imaging (MRI) scan. In some aspects, the techniques described herein relate to an apparatus, wherein the at least an electrogram includes at least an electrocardiogram (ECG). In some aspects, the techniques described herein relate to an apparatus, wherein the input data further includes one or more members from a group consisting of ultrasound data, point-of- care ultrasound (POCUS) data, intravascular ultrasound data, abdominal ultrasound data, at least a transthoracic echocardiogram (TTE), at least a transesophageal echocardiogram (TEE), at least an intracardiac echocardiogram (ICE), and at least a stress echocardiogram. In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured to: identify at least a target location pertaining to a medical procedure within the electro-anatomical mapping and highlight the at least a target location within the electro-anatomical mapping using the user interface. In some aspects, the techniques described herein relate to an apparatus, wherein: the medical procedure includes an ablation procedure and the electro-anatomical mapping is used as an initial mapping for the ablation procedure. In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured to: receive, from a navigation system, a location of a catheter and display, using the user interface, the location of the catheter on the electro-anatomical mapping. In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured modify the electro-anatomical mapping as a function of the 2 Attorney Docket No.1518-164PCT1 location of the catheter. In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured to: identify at least a medical feature within the electro- anatomical mapping and adjust at least a parameter pertaining to the medical procedure as a function of the at least a medical feature. In some aspects, the techniques described herein relate to an apparatus, wherein generating the electro-anatomical mapping includes aligning an electrical mapping with an anatomical mapping using fiducial point-based registration. In some aspects, the techniques described herein relate to an apparatus, wherein the electro-anatomical mapping includes a color-coded heat map. In some aspects, the techniques described herein relate to an apparatus, wherein: the exemplary medical images include historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures, the exemplary electrograms include historical electrograms pertaining to the plurality of entities, wherein the historical electrograms are collected prior to the one or more historical medical procedures and temporally correlated with the exemplary medical images, and the exemplary electro-anatomical mappings include historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures. In some aspects, the techniques described herein relate to an apparatus, wherein: the historical medical images include one or more of historical CT scans, historical MRI scans, and historical ultrasound data, the historical electrograms include historical ECGs, and the historical electro-anatomical mappings include historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures. In some aspects, the techniques described herein relate to a method for generating electro-anatomical mapping, the method including: receiving, by a processor, input data, wherein receiving the input data includes: receiving, from an imaging device, at least a medical image, and receiving, from a signal capturing device, at least an electrogram, generating, by the processor using at least a machine learning model, an electro-anatomical mapping as a function of the input data, wherein the at least a machine learning model is trained using electro- anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output, and 3 Attorney Docket No.1518-164PCT1 displaying, by the processor using a user interface, the electro-anatomical mapping, wherein generating the electro-anatomical mapping includes: generating a putative electro-anatomical mapping; validating the putative electro-anatomical mapping using a plurality of quality assurance parameters and creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation. In some aspects, the techniques described herein relate to a method, wherein the at least a medical image includes one or more of at least a computed tomography (CT) scan and at least a magnetic resonance imaging (MRI) scan. In some aspects, the techniques described herein relate to a method, wherein the at least an electrogram includes at least an electrocardiogram (ECG). In some aspects, the techniques described herein relate to a method, wherein the input data further includes one or more members from a group consisting of ultrasound data, point-of- care ultrasound (POCUS) data, intravascular ultrasound data, abdominal ultrasound data, at least a transthoracic echocardiogram (TTE), at least a transesophageal echocardiogram (TEE), at least an intracardiac echocardiogram (ICE), and at least a stress echocardiogram. In some aspects, the techniques described herein relate to a method, further including: identifying, by the processor, at least a target location pertaining to a medical procedure within the electro-anatomical mapping and highlighting, by the processor, the at least a target location within the electro-anatomical mapping using the user interface. In some aspects, the techniques described herein relate to a method, wherein: the medical procedure includes an ablation procedure and the electro-anatomical mapping is used as an initial mapping for the ablation procedure. In some aspects, the techniques described herein relate to a method, further including: receiving, by the processor from a navigation system, a location of a catheter; and displaying, by the processor using the user interface, the location of the catheter on the electro-anatomical mapping. In some aspects, the techniques described herein relate to a method, further including modifying the electro-anatomical mapping as a function of the location of the catheter. In some aspects, the techniques described herein relate to a method, further including: identifying, by the processor, at least a medical feature within the electro-anatomical mapping and adjusting, by the processor, at least a parameter pertaining to the medical procedure as a 4 Attorney Docket No.1518-164PCT1 function of the at least a medical feature. In some aspects, the techniques described herein relate to a method, wherein generating the electro-anatomical mapping includes aligning an electrical mapping with an anatomical mapping using fiducial point-based registration. In some aspects, the techniques described herein relate to a method, wherein the electro- anatomical mapping includes a color-coded heat map. In some aspects, the techniques described herein relate to a method, wherein: the exemplary medical images include historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures and the exemplary electro- anatomical mappings include historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures. In some aspects, the techniques described herein relate to a method, wherein: the historical medical images include one or more of historical CT scans, historical MRI scans, and historical ultrasound data and the historical electro-anatomical mappings include historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures. In some aspects, the techniques described herein relate to an apparatus for generating electro-anatomical mapping, the apparatus including: a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to: receive input data, wherein receiving the input data includes: receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram, generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, wherein the machine learning model has been trained using electro-anatomical training data including historical medical images collected prior to one or more historical medical procedures and historical electrograms collected prior to the one or more historical medical procedures correlated to historical electro-anatomical mappings collected during the one or more historical medical procedures, and display the electro-anatomical mapping using a user interface. In some aspects, the techniques described herein relate to an apparatus, wherein generating the electro-anatomical mapping includes: generating a putative electro-anatomical mapping; validating the putative electro-anatomical mapping using a plurality of quality assurance parameters and creating the electro-anatomical mapping by fine-tuning the putative 5 Attorney Docket No.1518-164PCT1 electro-anatomical mapping as a function of an outcome of the validation. In some aspects, the techniques described herein relate to an apparatus, wherein: exemplary medical images include historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures, exemplary electrograms include historical electrograms pertaining to the plurality of entities, wherein the historical signals are collected prior to the one or more historical medical procedures and temporally correlated with the exemplary medical images, and exemplary electro-anatomical mappings include historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures. In some aspects, the techniques described herein relate to an apparatus, wherein: the historical medical images include one or more of historical CT scans, historical MRI scans, and historical ultrasound data, the historical electrograms include historical ECGs, and the historical electro-anatomical mappings include historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures. In some aspects, the techniques described herein relate to an apparatus, wherein the at least an electrogram includes at least an electrocardiogram (ECG). In some aspects, the techniques described herein relate to an apparatus, wherein the input data further includes ultrasound data. In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured modify the electro-anatomical mapping as a function of location data of a catheter, wherein modifying the electro-anatomical mapping includes adjusting a color of a region in the electro-anatomical mapping corresponding to a catheter's position in response to changes in electrical potential. In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured to: identify at least a target location pertaining to a medical procedure within the electro-anatomical mapping and highlight the at least a target location within the electro-anatomical mapping using the user interface, updating a first view by replacing the first view with a second view as the location data of the catheter change, adjusting, using the location data of the catheter, a zoom level within the electro-anatomical map, correcting, using real-time data from the location data of the catheter, at least an error in the electro-anatomical map. 6 Attorney Docket No.1518-164PCT1 In some aspects, the techniques described herein relate to an apparatus, wherein: the medical procedure includes an ablation procedure, the ablation procedure including: receiving, from a navigation system, a location of a catheter and displaying, using the user interface, the location of the catheter on the electro-anatomical mapping; and the electro-anatomical mapping is used as an initial mapping for the ablation procedure. In some aspects, the techniques described herein relate to an apparatus, wherein validating a putative electro-anatomical mapping using a plurality of quality assurance parameters includes comparing, using the quality assurance parameters, the putative electro- anatomical mapping to one or more reference electro-anatomical mappings to determine a degree of correspondence between the putative electro-anatomical mapping and the reference electro- anatomical mappings. In some aspects, the techniques described herein relate to a method for generating electro-anatomical mapping, wherein the method includes: receiving, at a processor, input data, wherein receiving the input data includes: receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram, generating, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, wherein the machine learning model has been trained using electro-anatomical training data including historical medical images collected prior to one or more historical medical procedures and historical electrograms collected prior to the one or more historical medical procedures correlated to historical electro-anatomical mappings collected during the one or more historical medical procedures, and displaying the electro-anatomical mapping using a user interface. In some aspects, the techniques described herein relate to a method, wherein generating the electro-anatomical mapping includes: generating a putative electro-anatomical mapping, validating the putative electro-anatomical mapping using a plurality of quality assurance parameters, and creating the electro-anatomical mapping by fine-tuning the putative electro- anatomical mapping as a function of an outcome of the validation. In some aspects, the techniques described herein relate to a method, wherein: exemplary medical images include historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures, exemplary electrograms include historical electrograms pertaining to the plurality of entities, wherein the historical signals are collected prior to the one or more historical medical procedures and temporally correlated with 7 Attorney Docket No.1518-164PCT1 the exemplary medical images, and exemplary electro-anatomical mappings include historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures. In some aspects, the techniques described herein relate to a method, wherein: the historical medical images include one or more of historical CT scans, historical MRI scans, and historical ultrasound data, the historical electrograms include historical ECGs, and the historical electro-anatomical mappings include historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures. In some aspects, the techniques described herein relate to a method, wherein the at least an electrogram includes at least an electrocardiogram (ECG). In some aspects, the techniques described herein relate to a method, wherein the input data further includes ultrasound data. In some aspects, the techniques described herein relate to a method, wherein at least a processor is further configured modify the electro-anatomical mapping as a function of location data of a catheter, wherein modifying the electro-anatomical mapping includes adjusting a color of a region in the electro-anatomical mapping corresponding to a catheter's position in response to changes in electrical potential. In some aspects, the techniques described herein relate to a method, further including: identifying, using the processor, at least a target location pertaining to a medical procedure within the electro-anatomical mapping and highlighting, using the processor, the at least a target location within the electro-anatomical mapping using the user interface; updating a first view by replacing the first view with a second view as the location data of the catheter change; adjusting, using the location data of the catheter, a zoom level within the electro-anatomical map; and correcting, using real-time data from the location data of the catheter, at least an error in the electro-anatomical map. In some aspects, the techniques described herein relate to a method, wherein: the medical procedure includes an ablation procedure, the ablation procedure including: receiving, from a navigation system, a location of a catheter and displaying, using the user interface, the location of the catheter on the electro-anatomical mapping, and the electro-anatomical mapping is used as an initial mapping for the ablation procedure. In some aspects, the techniques described herein relate to a method, wherein validating a 8 Attorney Docket No.1518-164PCT1 putative electro-anatomical mapping using a plurality of quality assurance parameters includes comparing, using the quality assurance parameters, the putative electro-anatomical mapping to one or more reference electro-anatomical mappings to determine a degree of correspondence between the putative electro-anatomical mapping and the reference electro-anatomical mappings. In some aspects, the techniques described herein relate to an apparatus for generating electro-anatomical mapping, wherein the apparatus includes: a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to: receive input data, wherein the input data includes at least a medical image and at least an electrogram, generate an electro-anatomical mapping as a function of the input data, wherein generating the electro-anatomical mapping includes: generating a putative electro-anatomical mapping, validating the putative electro-anatomical mapping using a plurality of quality assurance parameters, and creating the electro-anatomical mapping by fine- tuning the putative electro-anatomical mapping as a function of an outcome of the validation, and display the electro-anatomical mapping using a user interface. In some aspects, the techniques described herein relate to an apparatus, wherein generating the electro-anatomical mapping further includes using a machine learning module to implement at least a machine learning model, wherein the machine learning model has been trained on electro-anatomical mapping training data including exemplary medical images and exemplary electrograms correlated to exemplary electro-anatomical mappings. In some aspects, the techniques described herein relate to an apparatus, wherein: validating the putative electro-anatomical mapping produces an outcome of validation and the outcome of validation includes a binary output. In some aspects, the techniques described herein relate to an apparatus, wherein: validating the putative electro-anatomical mapping produces an outcome of validation and the outcome of validation includes at least a confidence score. In some aspects, the techniques described herein relate to an apparatus, wherein generating the electro-anatomical mapping further includes: generating an electrical mapping using a mapping catheter and a mapping system and generating an anatomical mapping using an in vivo imaging system. In some aspects, the techniques described herein relate to an apparatus, wherein generating the electro-anatomical mapping further includes aligning the electrical mapping with 9 Attorney Docket No.1518-164PCT1 the anatomical mapping, where aligning the electrical mapping with the anatomical mapping includes: selecting at least a fiducial point in each of the electrical mapping and the anatomical mapping, applying at least a transformation to at least one of the electrical mapping and the anatomical mapping as a function of the at least a fiducial point, and adjusting the electrical mapping and the anatomical mapping to a common coordinate system as a function of the at least a transformation. In some aspects, the techniques described herein relate to an apparatus, wherein displaying the electro-anatomical mapping further includes applying a color-coded heat map to the electro-anatomical mapping, wherein applying a color-coded heat map to the electro- anatomical mapping includes: associating a first region of the electro-anatomical mapping with a first electrical activity, displaying the first region using a first color, associating a second region of the electro-anatomical mapping with a second electrical activity, and displaying the second region using a second color different from the first color. In some aspects, the techniques described herein relate to an apparatus, wherein the color coded-heat map includes a continuous color spectrum across spatial regions, the first color and the second color being selected from the continuous color spectrum such that adjacent regions within the electro-anatomical mapping transition smoothly between color values representing different electrical activities. In some aspects, the techniques described herein relate to an apparatus, wherein displaying the electro-anatomical mapping further includes highlighting at least a target location within the electro-anatomical mapping. In some aspects, the techniques described herein relate to an apparatus, wherein: the processor is further configured to receive, from a navigation system, a location of a catheter and displaying the electro-anatomical mapping further includes: displaying, on the user interface, a visual indicator of the catheter location overlaid on the electro-anatomical mapping and dynamically modifying at least one aspect of the electro-anatomical mapping as a function of the catheter location. In some aspects, the techniques described herein relate to a method for generating electro-anatomical mapping, wherein the method includes: receiving, by a processor, input data, wherein the input data includes at least a medical image and at least an electrogram: generating, using the processor, an electro-anatomical mapping as a function of the input data, wherein 10 Attorney Docket No.1518-164PCT1 generating the electro-anatomical mapping includes: generating a putative electro-anatomical mapping, validating the putative electro-anatomical mapping using a plurality of quality assurance parameters, and creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation, and displaying the electro-anatomical mapping using a user interface. In some aspects, the techniques described herein relate to a method, wherein generating the electro-anatomical mapping further includes using a machine learning module to implement at least a machine learning model, wherein the machine learning model has been trained on electro-anatomical mapping training data including exemplary medical images and exemplary electrograms correlated to exemplary electro-anatomical mappings. In some aspects, the techniques described herein relate to a method, wherein: validating the putative electro-anatomical mapping produces an outcome of validation and the outcome of validation includes a binary output. In some aspects, the techniques described herein relate to a method, wherein: validating the putative electro-anatomical mapping produces an outcome of validation and the outcome of validation includes at least a confidence score. In some aspects, the techniques described herein relate to a method, wherein generating the electro-anatomical mapping further includes: generating an electrical mapping using a mapping catheter and a mapping system and generating an anatomical mapping using an in vivo imaging system. In some aspects, the techniques described herein relate to a method, wherein generating the electro-anatomical mapping further includes aligning the electrical mapping with the anatomical mapping, where aligning the electrical mapping with the anatomical mapping includes: selecting at least a fiducial point in each of the electrical mapping and the anatomical mapping, applying at least a transformation to at least one of the electrical mapping and the anatomical mapping as a function of the at least a fiducial point, and adjusting the electrical mapping and the anatomical mapping to a common coordinate system as a function of the at least a transformation. In some aspects, the techniques described herein relate to a method, wherein displaying the electro-anatomical mapping further includes applying a color-coded heat map to the electro- anatomical mapping, wherein applying a color-coded heat map to the electro-anatomical 11 Attorney Docket No.1518-164PCT1 mapping includes: associating a first region of the electro-anatomical mapping with a first electrical activity, displaying the first region using a first color, associating a second region of the electro-anatomical mapping with a second electrical activity, and displaying the second region using a second color different from the first color. In some aspects, the techniques described herein relate to a method, wherein the color coded-heat map includes a continuous color spectrum across spatial regions, the first color and the second color being selected from the continuous color spectrum such that adjacent regions within the electro-anatomical mapping transition smoothly between color values representing different electrical activities. In some aspects, the techniques described herein relate to a method, wherein displaying the electro-anatomical mapping further includes highlighting at least a target location within the electro-anatomical mapping. In some aspects, the techniques described herein relate to a method, wherein: the method further includes receiving, from a navigation system, a location of a catheter and displaying the electro-anatomical mapping further includes: displaying, on the user interface, a visual indicator of the catheter location overlaid on the electro-anatomical mapping and dynamically modifying at least one aspect of the electro-anatomical mapping as a function of the catheter location. 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.1A is an exemplary embodiment of an apparatus for generating an electro-anatomical mapping; FIG.1B is an exemplary embodiment of a workflow of the apparatus in FIG.1A; FIG.2 is an illustration of an exemplary embodiment of an electrocardiogram (ECG); FIG.3 is an illustration of an exemplary embodiment of an intracardiac echocardiogram (ICE); FIG.4 is an illustration of an exemplary embodiments of a graphical user interface; FIG.5 is a flow diagram of an exemplary embodiment of an ICE image example generation process; FIG.6 illustrates an exemplary embodiment of a three-dimensional (3D) voxel occupancy representation; 12 Attorney Docket No.1518-164PCT1 FIG.7 is a diagram illustrating an exemplary embodiment of an overlaid heat map; FIG.8 is a diagram illustrating an exemplary embodiment of an overlaid heat map; FIG.9 is a block diagram of an exemplary embodiment of a machine learning process; FIG.10 is a block diagram of an exemplary embodiment of a neural network; FIG.11 is a block diagram of an exemplary embodiment of a node of a neural network; FIG.12 is an exemplary flow diagram illustrating a method for generating an electro-anatomical mapping; and FIG.13 is 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, aspects of the present disclosure are directed to apparatus and methods for generating electro-anatomical mapping. Apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to receive input data, wherein receiving the input data includes receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram. In one or more embodiments, at least a medical image may include at least a computed tomography (CT) scan and / or at least a magnetic resonance imaging (MRI) scan. In one or more embodiments, at least an electrogram may include at least an electrocardiogram (ECG). In some cases, input data and / or at least a medical image may include ultrasound data. Processor is further configured to generate, using at least a machine learning model, an electro-anatomical mapping as a function of input data, wherein the at least a machine learning model is trained using electro-anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro- anatomical mappings as output. In one or more embodiments, exemplary medical images may include historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures. In one or more embodiments, exemplary electrograms may include historical electrograms pertaining to plurality of entities, wherein the historical 13 Attorney Docket No.1518-164PCT1 electrograms are collected prior to one or more historical medical procedures and temporally correlated with exemplary medical images. In one or more embodiments, exemplary electro- anatomical mappings may include historical electro-anatomical mappings pertaining to plurality of entities and collected during one or more historical medical procedures. In one or more embodiments, generating electro-anatomical mapping may include generating a putative electro- anatomical mapping, validating the putative electro-anatomical mapping using a plurality of quality assurance parameters, and creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation. In one or more embodiments, electro-anatomical mapping may include a color-coded heat map. Processor is further configured to display electro-anatomical mapping using a user interface. In one or more embodiments, processor may be further configured to identify at least a target location pertaining to a medical procedure within electro-anatomical mapping and highlight the at least a target location within the electro-anatomical mapping using user interface. In some cases, medical procedure may include an ablation procedure and electro-anatomical mapping is used as an initial mapping for the ablation procedure. In some cases, processor is further configured to receive, from a navigation system, a location of a catheter and display, using user interface, the location of the catheter on electro-anatomical mapping. In some cases, processor may be further configured modify electro-anatomical mapping as a function of location of catheter. In some cases, processor may be further configured to identify at least a medical feature within electro- anatomical mapping and adjust at least a parameter pertaining to medical procedure as a function of the at least a medical feature. In one or more embodiments, generating electro-anatomical mapping may include aligning an electrical mapping with an anatomical mapping using fiducial point-based registration. Aspects of the present disclosure may be used to provide safe, affordable, and efficient clinical decision support. Aspects of the present disclosure may be used to provide real-time, adaptive guidance during medical procedures, such as cardiac ablation, without exposing patients to repeated discomfort. Aspects of the present disclosure may be used to promote the use of noninvasive or minimally invasive clinical tools for such medical procedures. Aspects of the present disclosure may be used to support image-guided surgery and therapy. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. 14 Attorney Docket No.1518-164PCT1 Referring now to FIG.1A, an exemplary embodiment 100a of an apparatus 100 for generating electro-anatomical mapping is illustrated. Apparatus 100 includes a processor 102. In one or more embodiments, processor 102 may include a computing device. Computing device could include any analog or digital control circuit, including an operational amplifier circuit, a combinational logic circuit, a sequential logic circuit, an application-specific integrated circuit (ASIC), a field programmable gate arrays (FPGA), or the like. Computing device may include a processor communicatively connected to a memory, as described above. Computing device may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor, and / or system on a chip as described in this disclosure. Computing device may include, be included in, and / or communicate with a mobile device such as a mobile telephone, smartphone, or tablet. Computing device may include a single computing device operating independently, or may include two or more computing device 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 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 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. Computing device may include but is not limited to, for example, a first 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 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. 15 Attorney Docket No.1518-164PCT1 Computing device 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 may be implemented, as a nonlimiting example, using a “shared nothing” architecture. With continued reference to FIG.1A, computing device 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 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 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. A person of ordinary skill 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. More details regarding computing devices will be described below. With continued reference to FIG.1A, apparatus 100 includes a memory 104 communicatively connected to processor 102, wherein the memory 104 contains instructions configuring the processor 102 to perform any processing steps described herein. For the purposes of 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, 16 Attorney Docket No.1518-164PCT1 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, using a bus or other facility for intercommunication between elements of a computing device. 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.1A, computing device may perform determinations, classification, and / or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. For the purposes of this disclosure, a “machine learning process” is a process that automatedly uses a body of data known as “training data” and / or a “training set” to generate an algorithm that will be performed by a processor module to produce outputs given data provided as inputs. This is in contrast to a nonmachine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. A machine learning process may utilize supervised, unsupervised, lazy-learning processes and / or neural networks. More details regarding computing devices and machine learning processes will be provided below. With continued reference to FIG.1A, apparatus 100 may include or be communicatively connected to a database. For the purposes of this disclosure, a “database” is an organized collection of data or a type of data store based on the use of a database management system (DBMS), the software that interacts with end users, applications, and the database itself to capture and analyze the data. Database 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 database that a person of ordinary skill in the art would recognize as suitable upon review of the entirety of this disclosure. Database may Alternatively, and / or additionally, 17 Attorney Docket No.1518-164PCT1 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 in this disclosure. Data entries in 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 database or another relational database. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in 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.1A, apparatus 100 may include or be communicatively connected to one or more electronic health records (EHRs). For the purposes of this disclosure, an electronic health record (EHR) is a comprehensive collection of records relating to the health history, diagnosis, or condition of a patient, relating to treatment provided or proposed to be provided to the patient, or relating to additional factors that may impact the health of the patient; elements within an EHR, once combined, may provide a detailed picture of patient's overall health. In one or more embodiments, medical information such as medical images may be deposited to and retrieved from one or more EHRs. In one or more embodiments, EHR may include demographic data of patient; for example, and without limitation, EHR may include basic information about patient such as name, age, gender, ethnicity, socioeconomic status, and / or the like. In one or more embodiments, each EHR may also include patient’s medical history; for example, and without limitation, EHR may include a detailed record of patient's past health conditions, medical procedures, hospitalizations, and illnesses such as surgeries, treatments, medications, allergies, and / or the like. In one or more embodiments, each EHR may include lifestyle information of patient; for example, and without limitation, EHR may include details about the patient's diet, exercise habits, smoking and alcohol consumption, and other behaviors that could impact patient’s health. In one or more embodiments, EHR may include patient’s family history; for example, and without limitation, EHR may include a record of hereditary diseases. In one or more embodiments, database may comprise a plurality of EHRs. In one or more embodiments, EHRs may be retrieved from a repository of similar nature as database. With continued reference to FIG.1A, processor 102 is configured to receive input data 18 Attorney Docket No.1518-164PCT1 106. For the purposes of this disclosure, “input data” are data or information that may be used as input to initiate subsequent processing steps. Input data 106 may include any type or form of data that contain medically relevant information, as recognized by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. Apparatus 100 may include or be communicatively connected to imaging device 108. Accordingly, receiving input data 106 includes receiving, from imaging device 108, at least a medical image 110. For the purposes of this disclosure, an “imaging device” is a device capable of recording a digital representation of an object. Imaging device 108 may include any type of imaging device accessible to a person of ordinary skill in the art, and / or deemed suitable by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. In one or more embodiments, imaging device 108 may include a camera. For the purposes of this disclosure, a “camera” is a single device, or an assembly of multiple devices, configured to detect at least one type of electromagnetic radiation and generate a graphical representation therefrom. As nonlimiting examples, a camera may detect visible light, infrared light, ultraviolet light, or X-ray. In one or more embodiments, a camera may include one or more optics. Nonlimiting examples of optics include spherical lenses, aspherical lenses, reflectors, polarizers, filters, windows, aperture stops, and the like. In one or more embodiments, a camera may include an image sensor. Exemplary image sensors include digital image sensors, such as without limitation charge-coupled device (CCD) sensors and complimentary metal-oxide-semiconductor (CMOS) sensors. As a nonlimiting example, a camera may include a remote camera device communicatively connected to a computing device, such as a portable camera connected to a desktop or laptop computer through either a cord or wireless connection. As another nonlimiting example, a camera may include a built-in camera integrated within a computing device, such as a built-in camera of a laptop computer. As another nonlimiting example, a camera may include a built-in camera integrated within a remote and / or portable device, such as a built-in camera of a smartphone or a tablet. With continued reference to FIG.1A, for the purposes of this disclosure, a “medical image” is an image containing medically relevant features or information. In one or more embodiments, at least a medical image 110 may include one or more computed tomography (CT) images or scans 112. For the purposes of this disclosure, computed tomography (CT) is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of a patient’s body. By taking a plurality of slices, a CT scan creates a detailed three-dimensional 19 Attorney Docket No.1518-164PCT1 (3D) representation of internal structures. In one or more embodiments, at least a medical image 110 may include at least a magnetic resonance imaging (MRI) scan 114. For the purposes of this disclosure, “magnetic resonance imaging (MRI)” is a noninvasive diagnostic technique that utilizes a combination of strong magnetic fields and radiofrequency waves to generate detailed images of the internal structures of an organ or a tissue. MRI typically involves placing a subject within a magnetic field, aligning the nuclear magnetization of hydrogen atoms in the body of the subject. Radiofrequency pulses are then applied to perturb this alignment. As hydrogen atoms relax back to their ground state, the emitted energy therefrom is captured to construct high- resolution images based on varying tissue densities and compositions. MRI is extensively used for visualizing soft tissues, including the brain, muscles, and cardiovascular structures. With continued reference to FIG.1A, for the purposes of this disclosure, an “image” is a visual representation of data. In one or more embodiments, an image may be a product of imaging device described above. In one or more embodiments, an image may contain digital information representing at least a physical scene, space, and / or object. In one or more embodiments, an image may be an optical image, such as without limitation an image of an object generated by at least an optic. In some cases, an image may be a digital representation of another image, such as a digital image of a printed photograph or the like captured using a built- in camera of a smartphone. Alternatively, an image may include a plurality of images arranged in sequence as a function of time, such as one or more videos. In some cases, an image may include a digital image. A digital image may be in a format such as jpeg, png, pdf, btmp, and the like. With continued reference to FIG.1A, apparatus 100 may include or be communicatively connected to a signal capturing device 116. Accordingly, receiving input data 106 includes receiving, from signal capturing device 116, at least an electrogram 118. For the purposes of this disclosure, a “signal capturing device” is a device capable of capturing a signal and one or more features therein. For the purposes of this disclosure, a “signal” is an intelligible representation of data that’s transmitted from one device to another. A signal may include an optical signal, a hydraulic signal, a pneumatic signal, a mechanical signal, an electric signal, a digital signal, an analog signal, and the like. In some cases, a signal may be used to communicate with a computing device, for example by way of one or more ports. In some cases, a signal may be transmitted and / or received by a computing device, for example, by way of an input / output port. An analog signal may be digitized, for example by way of an analog to digital converter. In some 20 Attorney Docket No.1518-164PCT1 cases, an analog signal may be processed, for example by way of any analog signal processing steps described in this disclosure, prior to digitization. In some cases, a digital signal may be used to communicate between two or more devices, including without limitation computing devices. In some cases, a digital signal may be communicated by way of one or more communication protocols, including without limitation internet protocol (IP), controller area network (CAN) protocols, serial communication protocols (e.g., universal asynchronous receiver-transmitter [UART]), parallel communication protocols (e.g., IEEE 128 [printer port]), and the like. With continued reference to FIG.1A, for the purposes of this disclosure, an “electrogram” is a diagnostic recording obtained from electrodes placed directly on or within one or more organs or tissues to measure their electrical activity. This recording captures the temporal sequence of electrical potentials generated by a conduction system of such organs or tissues, providing detailed information about their electrical behaviors. In addition to the heart, organs such as the brain, skeletal muscles, nerves, eyes, stomach, intestines, uterus, and bladder may also exhibit electrical activities. These activities are essential for various physiological functions and may be measured using specific diagnostic techniques such as electroencephalography (EEG), electromyography (EMG), nerve conduction studies (NCS), electroretinography (ERG), electrogastrography (EGG), and urodynamic studies, among others. As a nonlimiting example, for the heart, electrograms 118 may be utilized to identify and analyze arrhythmias, guide therapeutic interventions such as catheter ablation, and assess the effectiveness of treatments. The data acquired from electrograms 118 is crucial for diagnosing and managing various cardiac conditions, offering a precise and localized view of cardiac electrical activity. Additional details will be provided below. With continued reference to FIG.1A, in one or more embodiments, at least an electrogram 118 may include at least an electrocardiogram (ECG) 120. For the purposes of this disclosure, an “electrocardiogram (ECG)” is a recording of electrical activity of patient’s heart over a period of time; “ECG” and “ECG data” may be used interchangeably throughout this disclosure. In one or more embodiments, ECG data may include one or more recordings captured by a plurality (e.g., 12) of electrodes placed on patient’s skin. In one or more embodiments, ECG data may include information regarding a P wave, T wave, QRS complex, PR interval, ST segment, and / or the like, as described in detail below in this disclosure. In one or more 21 Attorney Docket No.1518-164PCT1 embodiments, ECG data may be used to identify specific cardiac events or phases of a cardiac cycle, e.g., isovolumic relaxation, ventricular filling, isovolumic contraction, and rapid ventricular ejection. In one or more embodiments, at least an electrogram 118 may include at least an electroencephalogram (EEG). For the purposes of this disclosure, an “electroencephalogram (EEG)” is an electrogram of the spontaneous electrical activity of the brain measured using small, metal discs (electrodes) attached to the scalp; it provides useful diagnostic information related to brain disorders. With continued reference to FIG.1A, in one or more embodiments, apparatus 100 may include or be communicatively connected to an ultrasonic imaging system. For the purposes of this disclosure, an “ultrasonic imaging system” is an image capture device capable of capturing at least an ultrasound image. In some cases, ultrasonic imaging system may render a graphic representation of a three-dimensional (3D) object by sending ultrasound waves and detecting their reflections as they interact with a plurality of features or structures with various values of density and / or reflectivity within the 3D object. In some cases, 3D object may include an organ such as the heart with complex internal structures including walls, chambers, blood vessels, among others. In some cases, ultrasonic imaging system may include at least an ultrasound transducer. For the purposes of this disclosure, an “ultrasound transducer” is a device capable of generating and / or receiving ultrasound waves or ultrasound signals. As a nonlimiting example, ultrasound transducer may include one or more transmitters capable of converting electric signals into ultrasound waves. As another nonlimiting example, ultrasound transducer may include one or more receivers capable of converting ultrasound waves into electrical signals. Additionally, and / or alternatively, as another nonlimiting example, ultrasound transducer may include one or more transceivers capable of both transmitting and receiving ultrasound waves. “Ultrasound transducer” and “ultrasound probe” may be used interchangeably throughout this disclosure. With continued reference to FIG.1A, in one or more embodiments, input data and / or at least a medical image 110 may include ultrasound data. Ultrasound data may pertain to the heart and include one or more echocardiograms. Alternatively, ultrasound data may pertain to other tissues or organs and include data such as without limitation abdominal ultrasound data, among others. For the purposes of this disclosure, an “echocardiogram” is an ultrasound image specifically pertaining to the heart of a subject and generated using ultrasonic imaging system, as described above. Accordingly, the imaging technique used to collect echocardiograms may be 22 Attorney Docket No.1518-164PCT1 called “echocardiography”. Exemplary types of echocardiography may include intracardiac echocardiography (ICE), point-of-care ultrasound (POCUS), transthoracic echocardiography (TTE), transesophageal echocardiography (TEE), stress echocardiography, and intravascular ultrasound, among others. For the purposes of this disclosure, “abdominal ultrasound” is a diagnostic medical procedure utilizing high-frequency sound waves to produce visual images of the organs and structures within the abdominal cavity. This non-invasive technique may involve placing a transducer on the patient's abdomen, which emits sound waves that reflect off internal organs, creating real-time images displayed on a monitor. The procedure may be used to evaluate and diagnose conditions related to the liver, gallbladder, pancreas, kidneys, spleen, and blood vessels, providing essential information for clinical assessment and treatment planning. With continued reference to FIG.1A, in some cases, an echocardiogram may include an intracardiac echocardiogram (ICE). For the purposes of this disclosure, an “intracardiac echocardiogram (ICE)” is a two-dimensional (2D) ultrasound image collected by inserting, using a catheter, an ultrasound transducer inside the heart. It represents the anatomy (i.e., walls, chambers, blood vessels, etc.) of at least part of the heart. In some cases, ICE may be collected by crossing the interatrial septum with a transseptal puncture to permit catheter access from the right atrium to the left atrium; alternatively, a catheter may access the left heart by retrograding through the aorta and passing the aortic valve to enter the left ventricle. For the purposes of this disclosure, a “catheter” is a medical device including a thin, flexible tube made from medical- grade materials that may be inserted into part of a patient’s body. Given its reduced size and flexible, noninvasive nature, a catheter may be configured to perform various functions such as collecting or transferring a clinical sample, administering a medicine or nutrient, providing a treatment for a disease, or performing a surgical procedure. Catheters are often manufactured for specific applications, such as cardiovascular, urological, gastrointestinal, neurovascular, and ophthalmic procedures. With continued reference to FIG.1A, in some cases, an echocardiogram may include a point-of-care ultrasound (POCUS). For the purposes of this disclosure, a “point-of-care ultrasound (POCUS)” is an ultrasound image collected using a medical technique that involves a portable, user-operated diagnostic imaging device and enables healthcare providers to perform real-time ultrasound examinations at the patient's bedside or in remote settings, without the need for specialized radiology departments. POCUS allows for immediate visualization of internal 23 Attorney Docket No.1518-164PCT1 structures and organs, facilitating rapid diagnosis, treatment decisions, and monitoring of various medical conditions. POCUS devices typically feature compact designs, integrated transducers, and user-friendly interfaces, enabling non-radiologist clinicians to obtain high-quality images. Applications of POCUS may include, but are not limited to, assessing cardiac function, detecting fluid collections, guiding needle placements, and evaluating trauma patients. The portability and ease of use of POCUS devices may significantly enhance clinical workflow, improve patient outcomes, and expand access to diagnostic imaging in diverse healthcare environments. With continued reference to FIG.1A, in some cases, an echocardiogram may include a transthoracic echocardiogram (TTE). For the purposes of this disclosure, a “transthoracic echocardiogram (TTE)” is a 2D ultrasound image of the heart collected by placing an ultrasound probe or ultrasound transducer on a patient’s chest or abdomen to collect various views of the heart. In some cases, an echocardiogram may include a transesophageal echocardiogram (TEE). For the purposes of this disclosure, a “transesophageal echocardiogram (TEE)” is a 2D ultrasound image of the heart collected by passing a specialized probe or catheter containing an ultrasound transducer at its tip into a patient’s esophagus. In some cases, an echocardiogram may include a stress cardiogram. For the purposes of this disclosure, a “stress echocardiogram” is a diagnostic medical procedure that assesses cardiac function by utilizing ultrasound imaging to visualize the heart’s activity under induced physical stress. This procedure involves obtaining echocardiographic images of the heart at rest, followed by images captured during or immediately after physical exercise, or pharmacologically induced stress if the patient is unable to exercise. Stress echocardiogram may be used to evaluate myocardial performance, detect ischemia (i.e., a case of restricted or reduced blood flow in part of a body), and identify areas of compromised blood flow, providing critical information for diagnosing and managing various cardiac conditions. With continued reference to FIG.1A, for the purposes of this disclosure, a “subject” or “patient” is a human or any individual organism, on whom or on which a procedure, study, or otherwise experiment, may be conducted. As nonlimiting examples, patient may include human patient with symptoms of atrial or ventricular fibrillation and / or undergoing cardiac ablation, an individual undergoing cardiac screening, a participant in a clinical trial, an individual with congenital heart disease, a heart transplant candidate, an individual receiving follow-up care after cardiac surgery, a healthy volunteer, an individual with heart failure, or the like. Additionally, 24 Attorney Docket No.1518-164PCT1 and / or alternatively, patient may include a pet or an animal model (i.e., an animal used to model certain medical conditions such as a laboratory rat). “Subject”, “patient”, and “entity” may be used interchangeably throughout this disclosure. With continued reference to FIG.1A, processor 102 is configured to generate an electro- anatomical mapping 122 as a function of input data 106. For the purposes of this disclosure, an “electro-anatomical mapping” or “electro-anatomical map” is a sophisticated diagnostic tool that merges electrical activity data with anatomical imaging to create a detailed, 3D representation of a conduction system pertaining to a tissue or organ. Electro-anatomical mapping is typically generated by inserting a catheter with electrodes into the heart to record electrical signals and integrating these electrical signals with anatomical imaging using specialized mapping software. This results in a detailed, three-dimensional representation of the heart's electrical conduction system, which may be crucial for diagnosing and treating cardiac arrhythmias. An electro- anatomical mapping may be used to accurately diagnose and treat cardiac arrhythmias by providing clinicians with precise information about the heart’s electrical pathways and structural features. With continued reference to FIG.1A, in one or more embodiments, one or more machine learning models may be used to perform certain function or functions of apparatus 100, such as generation of electro-anatomical mapping 122, as described below. Processor 102 may use a machine learning module to implement one or more algorithms as described herein or generate one or more machine learning models, as described below. However, 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 machine learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and / or relationship that then allows the 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 be retrieved from a database, selected from one or more EHRs, or be provided by a user. In one or more embodiments, machine learning module may obtain training data by querying a communicatively connected database that includes past inputs and outputs. Training data may 25 Attorney Docket No.1518-164PCT1 include inputs from various types of databases, resources, and / or user inputs and outputs correlated to each of those inputs, so that 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 nonlimiting 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 one or more embodiments, training data may include previous outputs such that one or more machine learning models may iteratively produce outputs. With continued reference to FIG.1A, processor 102 is configured to generate electro- anatomical mapping 122 using at least a machine learning model 124. At least a machine learning model 124 is trained using electro-anatomical mapping training data 126. Electro- anatomical mapping training data 126 include exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output. In one or more embodiments, exemplary medical images may include historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures (i.e., pre-procedural medical images). In some cases, historical medical images may include historical CT scans, historical MRI scans, historical ultrasound data, and / or the like. In one or more embodiments, exemplary electrograms may include historical electrograms pertaining to plurality of entities, wherein the historical electrograms are collected prior to one or more historical medical procedures (i.e., pre-procedural electrograms) and temporally correlated with exemplary medical images. In some cases, historical electrograms may include historical ECGs, EEGs, or the like. In one or more embodiments, exemplary electro-anatomical mappings may include historical electro-anatomical mappings pertaining to a plurality of patients or entities and collected during one or more historical medical procedures (i.e., procedural electro-anatomical mappings). In some cases, historical electro-anatomical mappings may include historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures. In some cases, 26 Attorney Docket No.1518-164PCT1 historical medical images, historical electrograms, and / or historical electro-anatomical mappings may be correlated with the same patient or group of patients. In some cases, historical medical images, historical electrograms, and / or historical electro-anatomical mappings may be collected within a certain time window before their respective medical procedures. As a nonlimiting example, this time window may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months before a scheduled medical procedure. Implementation of at least a machine learning model may be consistent with any type of machine learning model or algorithm described in this disclosure. In one or more embodiments, electro-anatomical mapping training data 126 may include data specifically synthesized for training purposes using one or more generative models. As a nonlimiting example, electro-anatomical mapping training data 126 may be extracted from medical literature. In one or more embodiments, one or more historic queries may be incorporated into electro-anatomical mapping training data 126 upon validation. In one or more embodiments, electro-anatomical mapping training data 126 may be retrieved from one or more databases, EHRs, and / or other repositories of similar nature, or be supplied as one or more user inputs. In one or more embodiments, at least a portion of electro-anatomical mapping training data 126 may be added, deleted, replaced, or otherwise updated as a function of one or more inputs from one or more users. With continued reference to FIG.1A, apparatus 100 may be used to provide clinical decision support for an ablation procedure. For the purposes of this disclosure, an “ablation procedure” or “cardiac ablation procedure” is a procedure used for treatment of irregular heartbeats (i.e., arrhythmias). It often uses heat or cold to create tiny scars in the heart that block faulty heart signals and help restore a healthy heartbeat. Ablation procedures that use heat are termed radiofrequency (RF) ablations or rhizotomies, whereas ablation procedures that use cold are termed cryo-ablations instead. In some cases, an ablation procedure may include a pulse field ablation procedure, which is a relatively new ablation procedure that induces programed cell death (i.e., apoptosis). In some cases, an ablation procedure may include a plurality of dosage parameters. Ablation procedure is most often done using catheters that are inserted through a blood vessel. Typical cases of arrhythmias that may potentially be treated using ablation procedures include atrial fibrillation, ventricular fibrillation, atrial flutter, and Wolff-Parkinson- White syndrome, among others. For the purposes of this disclosure, “atrial fibrillation” is a medical condition of an irregular (and often very rapid) heart rhythm in the upper chambers of 27 Attorney Docket No.1518-164PCT1 the heart (i.e., the two atria). As a result, blood doesn’t flow as well as it should from the atria to the lower chambers of the heart (i.e., the two ventricles). Atrial fibrillation may lead to blood clots in the heart and may increase the risk of stroke, heart failure, and other heart-related complications. Atrial fibrillation may be contrasted to ventricular fibrillation, wherein it is the lower heart chambers (i.e., the two ventricles) of the heart that contract in a very rapid and uncoordinated manner instead; as a result, the heart doesn't pump blood efficiently to the rest of the body. With continued reference to FIG.1A, in one or more embodiments, generating electro- anatomical mapping 122 may include generating a putative electro-anatomical mapping 122. For the purposes of this disclosure, a “putative electro-anatomical mapping” is a tentative electro- anatomical mapping 122 proposed by apparatus 100 and requires further validation 130. For the purposes of this disclosure, “validation” is a process of confirming whether hypothesis is correct or not based on an independent information source. Validation may be either automated or manual. In one or more embodiments, results of validation may be binary, i.e., “correct” vs. “incorrect”. In one or more embodiments, results of validation may be expressed on one or more continuous scales. As a nonlimiting example, results of validation may include one or more confidence scores, e.g., a 95 / 100 or a 5 / 5. Accordingly, processor 102 may be configured to validate putative electro-anatomical mapping 122 using a plurality of quality assurance (QS) parameters 132. For the purposes of this disclosure, a “quality assurance (QS) parameter” is a specific criterion or standard used to evaluate the quality and performance of a system, process, or product. These parameters are essential for ensuring that a system, process, or product meets predefined requirements and user expectations. QS parameters may encompass a range of attributes, including without limitation functionality, reliability, usability, efficiency, maintainability, and portability, among others. QS parameters may be measured through various testing methods, such as unit tests, integration tests, and performance tests, among others, to identify and address defects, enhance quality, and ensure compliance with industry standards. Processor 102 may then be further configured to create electro-anatomical mapping 122 by fine- tuning putative electro-anatomical mapping 122 as a function of an outcome of validation 130. With continued reference to FIG.1A, in one or more embodiments, electro-anatomical mapping 122 may include a color-coded heat map 134. For the purposes of this disclosure, a “color-coded heat map” is a visual representation that uses a plurality of different colors to 28 Attorney Docket No.1518-164PCT1 indicate the value of a variable across a plurality of spatial regions. In some cases, a first region of electro-anatomical mapping 122 may be associated with a first electrical activity, such as a first electrical potential, and a second region of the electro-anatomical mapping 122 may be associated with a second electrical activity different from the first electrical activity, such as a second electrical potential different from the first electrical potential; accordingly, the first region may be highlighted with a first color, and the second region may be highlighted with a second color different from the first color. As a nonlimiting example, a first color may be red to indicate a positive electrical potential, whereas a second color may be blue to indicate a negative electrical potential. In some cases, a continuum containing a plurality of colors, such as a spectrum that varies from red to purple, may be used across a plurality of regions without sharp transition in between. With continued reference to FIG.1A, in one or more embodiments, apparatus 100 may include or be communicatively connected to a display device 136. For the purposes of this disclosure, a “display device” is a device configured to show visual information. In some cases, a display device may include a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. A display device may include, but is not limited to, a smartphone, tablet, laptop, monitor, tablet, and the like. A display device may include a separate device that includes a transparent screen configured to display computer-generated images and / or information. In one or more embodiments, a display device may be configured to visually present data through a user interface or a graphical user interface (GUI) to at least a user, wherein the user may interact with the data through the user interface or GUI, as described below. In one or more embodiments, a user may view GUI through a display device. In one or more embodiments, a display device may be located on remote device, as described below. In one or more embodiments, a display device and imaging device 108 may be the same device or integrated within the same device, such as a laptop, a smartphone or a tablet. Additional details will be provided below in this disclosure through nonlimiting examples. With continued reference to FIG.1A, display device 136 may include a remote device. For the purposes of this disclosure, a “remote device” is a computer device separate and distinct from apparatus 100. For example, and without limitation, a remote device may include a smartphone, a tablet, a laptop, a desktop computer, or the like. In one or more embodiments, a 29 Attorney Docket No.1518-164PCT1 remote device may be communicatively connected to apparatus 100 such as, for example, through network communication, through Bluetooth communication, and / or the like. In one or more embodiments, processor 102 may receive input data 106 and / or initiate one or more of subsequent steps through a remote device. In one or more embodiments, one or more inputs from one or more users may be submitted through a user interface, such as a GUI, displayed using a remote device, as described below. With continued reference to FIG.1A, apparatus 100 includes or is communicatively connected to a user interface 138. Accordingly, processor 102 is configured to display electro- anatomical mapping 122 using user interface 138. For the purposes of this disclosure, a “user interface” is a means by which a user and a computer system interact, for example, using input devices and software. User interface 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, or the like. In one or more embodiments, a user may interact with a user interface using computing device distinct from and communicatively connected to processor 102, such as a smartphone, tablet, or the like operated by the user. A user interface may include one or more graphical locator and / or cursor facilities allowing user to interact with graphical models and / or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and / or other manual data entry device. For the purposes of this disclosure, a “graphical user interface (GUI)” is a type of user interface that allows end users to interact with electronic devices through visual representations. In one or more embodiments, a GUI may include icons, menus, other visual indicators or representations (graphics), audio indicators such as primary notation, 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 as a pull-down menu. A menu may include a context menu that appears only when user performs a specific action. Files, programs, web pages, and the like may be represented using a small picture within a GUI. In one or more embodiments, a GUI may include a graphical visualization of a user profile and / or the like. In one or more embodiments, processor 102 may be configured to modify and / or update a GUI as a function of at least an input or the like by populating a user interface data structure and visually presenting data through modification of the GUI. With continued reference to FIG.1A, in one or more embodiments, a GUI may contain 30 Attorney Docket No.1518-164PCT1 one or more interactive elements. For the purposes of this disclosure, an “interactive element” is an element within a GUI that allows for communication with processor 102 by one or more users. For example, and without limitation, interactive elements may include a plurality of tabs wherein selection of a particular tab, such as for example, by using a fingertip, may indicate to a system to perform a particular function and display the result through a GUI. In one or more embodiments, interactive element may include tabs within a GUI, wherein the selection of a particular tab may result in a particular function. In one or more embodiments, interactive elements may include words, phrases, illustrations, and the like to indicate a particular process that one or more users would like a system to perform. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which user interfaces, GUIs, and / or elements thereof may be implemented and / or used as described in this disclosure. With continued reference to FIG.1A, in one or more embodiments, display device 136 and / or remote device may be configured to display at least an event handler graphic corresponding to at least an event handler. For the purposes of this disclosure, an “event handler graphic” is a graphical element with which user interacts using display device 136 and / or remote device to enter data, for instance and without limitation, for input data 106 or the like as described above. An event handler graphic may include, without limitation, a button, a link, a checkbox, a text entry box and / or window, a drop-down list, a slider, or any other event handler graphic deemed suitable by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. For the purposes of this disclosure, an “event handler” is a module, data structure, function, and / or routine that performs an action on display device 136 and / or remote device in response to one or more user inputs. For instance, and without limitation, an event handler may record data corresponding to user selections of previously populated fields such as drop-down lists and / or text auto-complete and / or default entries, data corresponding to user selections of checkboxes, radio buttons, or the like, potentially along with automatically entered data triggered by such selections, user entry of textual data using a keyboard, touchscreen, speech-to-text program, or the like. An event handler may generate prompts for further information, may compare data to validation rules such as requirements that the data in question be entered within certain numerical ranges, and / or may modify data and / or generate warnings to user in response to such requirements. An event handler may convert data into expected and / or desired formats, for instance such as date formats, currency entry formats, name formats, or the 31 Attorney Docket No.1518-164PCT1 like. An event handler may transmit data from a remote device to computing device. With continued reference to FIG.1A, in one or more embodiments, an event handler may include a cross-session state variable. For the purposes of this disclosure, a “cross-session state variable” is a variable recording data entered on remote device during a previous session. Such data may include, for instance, previously entered text, previous selections of one or more elements as described above, or the like. For instance, a cross-session state variable data may represent a search that user entered in a past session. A cross-session state variable may be saved using any suitable combination of client-side data storage on remote device and server-side data storage on computing device; for instance, data may be saved wholly or in part as a “cookie” which may include data or an identification of remote device to prompt provision of the cross- session state variable by the computing device, which may store the data on the computing device. Alternatively, or additionally, computing device may use login credentials, device identifier, and / or device fingerprint data to retrieve cross-session state variable, which the computing device may transmit to a remote device. A Cross-session state variable may include at least a prior session datum. A prior session datum may include any element of data that may be stored in cross-session state variable. An event handler graphic may be further configured to display at least a prior session datum, for instance and without limitation, by auto-populating user query data from previous sessions. With continued reference to FIG.1A, in one or more embodiments, processor 102 and / or computing device may configure display device 136 and / or remote device to generate a graphical view. For the purposes of this disclosure, a “graphical view” is a data structure that results in display of one or more graphical elements on a screen. A graphical view may include at least a display element. For the purposes of this disclosure, a “display element” is an image that a program and / or data structure cause to be displayed. Display elements may include, without limitation, windows, pop-up boxes, web browser pages, display layers, and / or any other display element deemed relevant by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. A graphical view may include at least a selectable event graphic corresponding to one or more selectable event handlers. For the purposes of this disclosure, a “selectable event graphic” is a graphical element that, upon selection, will trigger an action to be performed. Selection may be performed using a cursor or other locator as manipulated using a locator device such as a mouse, touchscreen, track pad, joystick, or the like. As a nonlimiting example, a 32 Attorney Docket No.1518-164PCT1 selectable event graphic may include a redirection link, defined as a hyperlink, button, image, portion of an image, and / or other graphic containing or referring to a uniform resource locator (URL) and / or other resource locator to another graphical view including without limitation buttons, and / or to a process that performs navigation to such URL and / or other resource locator upon selection of selectable event graphic. Redirection may be performed using any event handler, including without limitation event handlers detecting the click of a mouse or other locator, access of redirection link using a touchscreen, the selection of any key, mouseover events, or the like. With continued reference to FIG.1A, in one or more embodiments, processor 102 may be further configured to identify at least a target location 140 pertaining to a medical procedure 142 within electro-anatomical mapping 122. For the purposes of this disclosure, a “target location” is a location within a tissue or an organ that is of medical interest. Such medical interest may be pertaining to one or more specific steps to perform in a medical procedure. In some cases, medical procedure 142 may include ablation procedure, consistent with details described above. In some cases, electro-anatomical mapping 122 may be used as an initial mapping for an ablation procedure. Accordingly, processor 102 may be configured to highlight at least a target location 140 within electro-anatomical mapping 122 using user interface 138, consistent with details described above. With continued reference to FIG.1A, in some cases, processor 102 may be configured to receive, from a navigation system 144, a location 146 of a catheter. For the purposes of this disclosure, a “navigation system” is a system that monitors an exact position and / or orientation or a medical device with respect to a tissue or an organ, thereby providing guidance regarding subsequent steps to be performed by the medical device. Accordingly, processor 102 may be configured to display, using user interface 138, location 146 of catheter on electro-anatomical mapping 122, consistent with details described above. In some cases, processor 102 may be further configured modify one or more aspects of electro-anatomical mapping 122 as a function of location 146 of the catheter. As a nonlimiting example, processor 102 may be configured to replace or update a first view with a second view different from the first view, in user interface 138, as a catheter translates, rotates, or otherwise moves from a first location / angle to a second location / angle different from the first location / angle, thereby providing real-time navigation. As another nonlimiting example, processor 102 may be configured to zoom in or zoom out at certain 33 Attorney Docket No.1518-164PCT1 locations or regions within electro-anatomical mapping 122. As another nonlimiting example, processor 102 may be configured to correct one or more errors or inaccuracies within electro- anatomical mapping 122 using up-to-date information received by a catheter. With continued reference to FIG.1A, in one or more embodiments, processor 102 may be further configured to identify at least a medical feature 148 within electro-anatomical mapping 122. For the purposes of this disclosure, a “medical feature” is a structural or functional characteristic that describes one or more aspects regarding a patient’s health. In some cases, a medical feature may include one or more specific anatomical traits relevant to the successful completion of a medical procedure. As nonlimiting examples, medical features may include a presence of thrombus in the left atrial appendage of the heart, a presence of calcification at a heart valve, a thickness including an abnormal thickness of a heart wall, an abnormal heart valve anatomy or function, among others. Accordingly, in some cases, processor 102 may be configured to adjust at least a parameter 150 pertaining to medical procedure 142 as a function of at least a medical feature. For example, in the case of ablation procedure, adjustable parameters may include without limitation voltage, pulse duration, frequency, pulse width, amplitude, power of ablation, total energy delivered, total treatment time, energy delivered to a particular location, treatment time at a particular location, current, average power, peak power, and biphasic vs monophasic pulse delivery, among others. In some cases, processor 102 may be configured to alert a medical professional operating a catheter regarding one or more medical features that should be avoided. With continued reference to FIG.1A, in one or more embodiments, one or more machine learning models may be used to perform a certain function or functions of apparatus 100, such as determination of at least a medical feature. In some cases, this may include a machine learning module that implements one or machine learning models. In an embodiment, machine learning module may be configured to generate a multimodal neural network that combines various neural network architectures described herein. In a nonlimiting example, multimodal neural network may combine LSTM for time-series analysis with GPT models for natural language processing. Such fusion may be applied by processor 102 and / or computing device to generate synthetic images, medical features, or the like. In one or more embodiments, multimodal neural network may also include a hierarchical multimodal neural network, wherein the hierarchical multimodal neural network may involve a plurality of layers of integration. For instance, and 34 Attorney Docket No.1518-164PCT1 without limitation, different models may be combined at various stages of the network. Convolutional neural network (CNN) may be used for image feature extraction, followed by LSTMs for sequential pattern recognition, and a MDN at the end for probabilistic modeling. Other exemplary embodiments of multimodal neural network may include, without limitation, ensemble-based multimodal neural network, cross-modal fusion, adaptive multimodal network, among others. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various multimodal neural networks and combinations thereof that may be implemented by apparatus 100 in accordance with this disclosure. With continued reference to FIG.1A, in one or more embodiments, processor 102 may perform one or more functions of apparatus 100, such as training an image processing machine learning model, by using optical character recognition (OCR) to read digital files and extract information therein. In one or more embodiments, OCR may include automatic conversion of images (e.g., typed, handwritten, or printed text) into machine-encoded text. In one or more embodiments, recognition of at least a keyword from an image component may include one or more processes, including without limitation OCR, optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In one or more embodiments, OCR may recognize written text one glyph or character 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.1A, in one or more embodiments, OCR may employ preprocessing of image components. Preprocessing 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., homography or affine transform) to an image component 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 35 Attorney Docket No.1518-164PCT1 way of separating text (or any other desired image component) from the 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, 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 a 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 the aspect ratio and / or scale of image component. With continued reference to FIG.1A, in one or more embodiments, an OCR process may include an OCR algorithm. Exemplary OCR algorithms include matrix-matching processes and / or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by-pixel basis. In one or more embodiments, 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 image component. Matrix matching may also rely on a stored glyph being in a similar font and at the same scale as input glyph. With continued reference to FIG.1A, 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 features. Exemplary nonlimiting features may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In one or more embodiments, feature extraction may reduce the dimensionality of representation and may make the recognition process computationally more efficient. In one or more embodiments, extracted features can be compared with an abstract vector-like representation of a character, which might be reduced to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In one or more embodiments, machine learning process like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) can be used to compare image 36 Attorney Docket No.1518-164PCT1 features with stored glyph features and choose a nearest match. OCR may employ any machine learning process described in this disclosure. Exemplary nonlimiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multi-language, open-source OCR system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is a free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States. With continued reference to FIG.1A, in one or more embodiments, OCR may employ a two-pass approach to character recognition. Second pass may include adaptive recognition and use letter shapes recognized with high confidence on a first pass to better recognize remaining letters on a 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 includes OCRopus. The development of OCRopus is led by the German Research Center for Artificial Intelligence in Kaiserslautern, Germany. In one or more embodiments, OCR software may employ neural networks, for example, deep neural networks, as described in this disclosure below. With continued reference to FIG.1A, in one or more embodiments, OCR may include post-processing. For example, OCR accuracy can 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 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 some cases, an output stream may be a plain text stream or file of characters. In one or more embodiments, an OCR may preserve an original layout of visual verbal 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 use of a priori knowledge of grammar for a language being recognized. For example, OCR process may apply grammatical rules 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. A person of ordinary skill in the art will recognize how to apply the aforementioned technologies to extract information from a digital file upon reviewing the entirety of this disclosure. 37 Attorney Docket No.1518-164PCT1 With continued reference to FIG.1A, in one or more embodiments, a computer vision module configured to perform one or more computer vision tasks such as, without limitation, object recognition, feature detection, edge / corner detection thresholding, or machine learning process may be used to recognize specific features or attributes. For the purposes of this disclosure, a “computer vision module” is a computational component designed to perform one or more computer vision, image processing, and / or modeling tasks. Computer vision module may include an image processing module, wherein images may be pre-processed using the image processing module. For the purposes of this disclosure, an “image processing module” is a component designed to process digital images such as images described herein. For example, and without limitation, image processing module may be configured to compile a plurality of images of a multi-layer scan to create an integrated image. In one or more embodiments, image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques as described below. In one or more embodiments, computer vision module may also include hardware components such as, without limitation, one or more graphics processing units (GPUs) that can accelerate the processing of a large number of images. In one or more embodiments, computer vision module may receive one or more digital files containing one or more reference attributes from a data repository and generate one or more labels as a function of the received one or more reference attributes. In one or more embodiments, to generate a plurality of labels, computer vision module may be configured to compare one or more reference attributes against the statistical data of the one or more reference attributes and attach one or more labels as a function of the comparison, as described below. With continued reference to FIG.1A, in one or more embodiments, computer vision module may be implemented with one or more image processing libraries such as, without limitation, OpenCV, PIL / Pillow, ImageMagick, and the like. In a nonlimiting example, in order to generate one or more labels and / or recognize one or more reference attributes, one or more image processing tasks, such as noise reduction, contrast enhancement, intensity normalization, image segmentation, and / or the like, may be performed by computer vision module on a plurality of images to isolate certain features or components from the rest. In one or more embodiments, one or more machine learning models may be used to perform segmentations, for example, and without limitation, a U-net (i.e., a convolution neural network containing a contracting path as an 38 Attorney Docket No.1518-164PCT1 encoder and an expansive path as a decoder, wherein the encoder and the decoder forms a U- shaped structure). A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various image processing, computer vision, and modeling tasks that may be performed by processor 102.With continued reference to FIG.1A, in one or more embodiments, one or more functions of apparatus 100 may involve a use of image classifiers to classify images within any data described in this disclosure. For the purposes of this disclosure, an “image classifier” is a machine learning model that sorts inputs of image information into categories or bins of data, outputting the categories or bins of data and / or labels associated therewith. Image classifier may include a mathematical model, a neural net, or a program generated by a machine learning algorithm known as a “classification algorithm”, as described in further detail below. Image classifier may be configured to output at least a datum that labels or otherwise identifies a set of images that are clustered together, found to be close under a distance metric as described below, or the like. Computing device and / or another device may generate image classifier using a classification algorithm. For the purposes of this disclosure, a classification algorithm is a process whereby computing device derives a classifier from training data. 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. In one or more embodiments, processor 102 may use image classifier to identify a key image in any data described in this disclosure. For the purposes of this disclosure, a “key image” is an element of visual data used to identify and / or match elements to each other. In one or more embodiments, key image may include part of a medical image such as a CT scan, an MRI scan, or the like, with features that unambiguously identify the type of a medical image. Image classifier may be trained with binarized visual data that have already been classified to determine key images in any other data described in this disclosure. For the purposes of this disclosure, “binarized visual data” are visual data that are described in a binary format. For example, binarized visual data of a photo may comprise ones and zeroes, wherein the specific sequence of ones and zeros may be used to represent the photo. Binarized visual data may be used for image recognition wherein a specific sequence of ones and zeroes may indicate a product present in the image. An image 39 Attorney Docket No.1518-164PCT1 classifier may be consistent with any classifier as discussed herein. An image classifier may receive input data (e.g., medical images 110) described in this disclosure and output a key image with the data. In one or more embodiments, image classifier may be used to compare visual data in one data set with visual data in another data set. With continued reference to FIG.1A, processor 102 may be configured to perform feature extraction on at least a medical image 110 or the like. For the purposes of this disclosure, “feature extraction” is a process of transforming an initial data set into informative measures and values. For example, feature extraction may include a process of determining one or more geometric features of an anatomic structure. In one or more embodiments, feature extraction may be used to determine one or more spatial relationships within a drawing that may be used to uniquely identify one or more features. In one or more embodiments, processor 102 may be configured to extract one or more regions of interest, wherein the regions of interest may be used to extract one or more features using one or more feature extraction techniques. With continued reference to FIG.1A, processor 102 may be configured to perform one or more of its functions, such as identification of at least a medical feature 148, using a feature learning algorithm. For the purposes of this disclosure, a “feature learning algorithm” is a machine learning algorithm that identifies associations between elements of data in a data set where particular outputs and / or inputs are not specified. Data set may include without limitation a training data set. For instance, and without limitation, a feature learning algorithm may detect co-occurrences of elements of data, as defined above, with each other. Computing device may perform feature learning algorithm by dividing elements or sets of data into various sub- combinations of such data to create new elements of data and evaluate which elements of data tend to co-occur with which other elements. In one or more embodiments, feature learning algorithm may perform clustering of data. With continued reference to FIG.1A, feature learning and / or clustering algorithm may be implemented, as a nonlimiting example, using a k-means clustering algorithm. For the purposes of this disclosure, a “k-means clustering algorithm” is a type of cluster analysis that partitions n observations or unclassified cluster data entries into k clusters in which each observation or unclassified cluster data entry belongs to the cluster with the nearest mean. For the purposes of this disclosure, “cluster analysis” is a process that includes grouping a set of observations or data entries in way that observations or data entries in the same group or cluster are more similar to 40 Attorney Docket No.1518-164PCT1 each other than to those in other groups or clusters. Cluster analysis may be performed by various cluster models that include connectivity models such as hierarchical clustering, centroid models such as k-means, distribution models such as multivariate normal distribution, density models such as density-based spatial clustering of applications with nose (DBSCAN) and ordering points to identify the clustering structure (OPTICS), subspace models such as biclustering, group models, graph-based models such as a clique, signed graph models, neural models, and the like. Cluster analysis may include hard clustering, whereby each observation or unclassified cluster data entry belongs to a cluster or not. Cluster analysis may include soft clustering or fuzzy clustering, whereby each observation or unclassified cluster data entry belongs to each cluster to a certain degree such as for example a likelihood of belonging to a cluster; for instance, and without limitation, a fuzzy clustering algorithm may be used to identify clustering of elements of a first type or category with elements of a second type or category, and vice versa, as described below. Cluster analysis may include strict partitioning clustering, whereby each observation or unclassified cluster data entry belongs to exactly one cluster. Cluster analysis may include strict partitioning clustering with outliers, whereby observations or unclassified cluster data entries may belong to no cluster and may be considered outliers. Cluster analysis may include overlapping clustering whereby observations or unclassified cluster data entries may belong to more than one cluster. Cluster analysis may include hierarchical clustering, whereby observations or unclassified cluster data entries that belong to a child cluster also belong to a parent cluster. With continued reference to FIG.1A, computing device may generate a k-means clustering algorithm by receiving unclassified data and outputting a definite number of classified data entry clusters, wherein the data entry clusters each contain cluster data entries. K-means algorithm may select a specific number of groups or clusters to output, identified by a variable “k”. Generating k-means clustering algorithm includes assigning inputs containing unclassified data to a “k-group” or “k-cluster” based on feature similarity. Centroids of k-groups or k-clusters may be utilized to generate classified data entry cluster. K-means clustering algorithm may select and / or be provided “k” variable by calculating k-means clustering algorithm for a range of k values and comparing results. K-means clustering algorithm may compare results across different values of k as the mean distance between cluster data entries and cluster centroid. K- means clustering algorithm may calculate mean distance to a centroid as a function of k value, 41 Attorney Docket No.1518-164PCT1 and the location of where the rate of decrease starts to sharply shift, which may be utilized to select a k value. Centroids of k-groups or k-cluster include a collection of feature values which are utilized to classify data entry clusters containing cluster data entries. K-means clustering algorithm may act to identify clusters of closely related data, which may be provided with user cohort labels; this may, for instance, generate an initial set of user cohort labels from an initial set of data, and may also, upon subsequent iterations, identify new clusters to be provided new labels, to which additional data may be classified, or to which previously used data may be reclassified. With continued reference to FIG.1A, generating a k-means clustering algorithm may include generating initial estimates for k centroids which may be randomly generated or randomly selected from unclassified data input. K centroids may be utilized to define one or more clusters. K-means clustering algorithm may assign unclassified data to one or more k- centroids based on the squared Euclidean distance by first performing a data assigned step of unclassified data. K-means clustering algorithm may assign unclassified data to its nearest centroid based on the collection of centroids ^^^^ of centroids in set ^^. Unclassified data may beassigned to a cluster based on ^^^^^^^^^^^^^^ ∋ ^ ^^^^^^^^^^^^^, ^^^ଶ, where ^^^^^^^^^^^^ includes argument ofthe minimum, ^^^^ includes a collection of centroids in a set ^^, and ^^^^^^^^ includes standard Euclidean distance. K-means clustering module may then recompute centroids by taking a meanof all cluster data entries assigned to a centroid’s cluster. This may be calculated based on ^^^^ ൌ1 / |^^^^|∑ ^^^^ ∋ ^^^^ ௫^. K-means clustering algorithm may continue to repeat these calculations untila stopping criterion has been satisfied such as when cluster data entries do not change clusters, the sum of the distances have been minimized, and / or some maximum number of iterations has been reached. With continued reference to FIG.1A, k-means clustering algorithm may be configured to calculate a degree of similarity index value. For the purposes of this disclosure, a “degree of similarity index value” is a distance measured between each data entry cluster generated by k- means clustering algorithm and a selected element. Degree of similarity index value may indicate how close a particular combination of elements is to being classified by k-means algorithm to a particular cluster. K-means clustering algorithm may evaluate the distances of the combination of elements to the k-number of clusters output by k-means clustering algorithm. Short distances between an element of data and a cluster may indicate a higher degree of similarity between the 42 Attorney Docket No.1518-164PCT1 element of data and a particular cluster. Longer distances between an element and a cluster may indicate a lower degree of similarity between the element to be compared and / or clustered and a particular cluster. With continued reference to FIG.1A, k-means clustering algorithm selects a classified data entry cluster as a function of the degree of similarity index value. In one or more embodiments, k-means clustering algorithm may select a classified data entry cluster with the smallest degree of similarity index value indicating a high degree of similarity between an element and the data entry cluster. Alternatively, or additionally, k-means clustering algorithm may select a plurality of clusters having low degree of similarity index values to elements to be compared and / or clustered thereto, indicative of greater degrees of similarity. Degree of similarity index values may be compared to a threshold number indicating a minimal degree of relatedness suitable for inclusion of a set of element data in a cluster, where degree of similarity indices a-n falling under the threshold number may be included as indicative of high degrees of relatedness. The above-described illustration of feature learning using k-means clustering is included for illustrative purposes only and should not be construed as limiting potential implementation of feature learning algorithms; a person of ordinary skills in the art, upon reviewing the entirety of this disclosure, will be aware of various additional or alternative feature learning approaches, such as particle swarm optimization (PSO) and generative adversarial network (GAN) that may be used consistently with this disclosure. With continued reference to FIG.1A, in one or more embodiments, processor 102 may use an image recognition algorithm to determine patterns within an image. In one or more embodiments, image recognition algorithm may include an edge-detection algorithm, which may detect one or more shapes defined by edges. For the purposes of this disclosure, an “edge detection algorithm” is or includes a mathematical method that identifies points in a digital image at which the image brightness changes sharply and / or has discontinuities. In one or more embodiments, such points may be organized into straight and / or curved line segments, which may be referred to as “edges”. Edge detection may be performed using any suitable edge detection algorithm, including without limitation Canny edge detection, Sobel operator edge detection, Prewitt operator edge detection, Laplacian operator edge detection, and / or differential edge detection. Edge detection may include phase congruency-based edge detection, which finds all locations of an image where all sinusoids in the frequency domain, for instance when 43 Attorney Docket No.1518-164PCT1 generated using a Fourier decomposition, may have matching phases which may indicate a location of an edge. With continued reference to FIG.1A processor 102 may be configured to receive, from imaging device 108, at least a query image, and identify a pose of an anatomical structure as a function of the at least a query image. For the purposes of this disclosure, a “pose” is a set of geometric parameters that collectively describe the location and orientation of an object with respect to the position of image capture device or vice versa. For the purposes of this disclosure, an “anatomical structure” is an organ, a tissue, or a portion thereof that is capable of being analyzed by apparatus 100 for medically relevant information. Anatomical structure may include any type of tissue, organ, or the like to which apparatus 100 may be applicable, as recognized by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. Nonlimiting examples of anatomical structure may include at least a portion of a heart, a liver, a lung, a kidney, a thyroid, a prostate, a uterus, or the like, consistent with details described above. In one or more embodiments, identifying pose of an anatomical structure may include locating at least a point of view (POV) as a function of query image and determining a view angle (VA) as a function of the at least a POV, wherein the at least a POV and the VA define at least a portion the anatomical structure. Portion of anatomical structure may include a portion of the heart, such as the left atrium. For the purposes of this disclosure, a “point of view (POV)” is a specific spatial location or origin from which an image or scene is observed or captured. For the purposes of this disclosure, a “view angle (VA)” is an angular orientation or direction defined by one or more ^^ and ^^ angles that is projected from POV. In some embodiments, VA may be expressed using cylindrical coordinates. In some embodiments, VA may be expressed in spherical coordinates. In some cases, identifying pose of anatomical structure may include retrieving a 3D model or creating a 3D model from plurality of images to position query image therein.3D model may include a 3D heart model. As a nonlimiting example, 3D model / 3D heart model may be created using a statistical shape model (SSM). For the purposes of this disclosure, a statistical shape model (SSM) is a mathematical representation of the shape variations within a collection of objects. SSM uses statistical methods to capture the common features and variations of shapes in a dataset. SSM is often applied in fields such as medical imaging, computer vision, and biomechanics. In some cases, construction of SSM may include a principal component analysis (PCA). For the purposes of this disclosure, principal component analysis (PCA) is a 44 Attorney Docket No.1518-164PCT1 dimensionality reduction technique used to identify the main axes of variation in the data. In the context of SSM, PCA may be applied to coordinates of landmark points on the shapes to find these principal components, which are also known as modes of variation. Construction of SSM may include converting a plurality of pixels to a set of statistical shape eigenvalues. As a nonlimiting example, when PCA is performed on a set of shape data, it may result in a set of eigenvalues and eigenvectors. These eigenvalues may represent the amount of variance captured by each principal component (eigenvector). Each eigenvalue in an SSM may indicate how much of the total shape variation is explained by the corresponding principal component. As a nonlimiting example, larger eigenvalues may indicate that its corresponding principal component captures more of the shape variation in the dataset; accordingly, components with small eigenvalues may contribute little to the overall variance and can often be excluded to simplify the model without losing significant information. If a first eigenvalue is significantly larger than the others, it means the first principal component accounts for most of the shape variation. By examining these eigenvalues, one can decide how many principal components are needed to adequately describe the shape variation. These eigenvalues, together with the corresponding eigenvectors, may be used to reconstruct shapes from a model. A shape may be approximated as a mean shape plus a weighted sum of the principal components, wherein the weights are determined by these eigenvalues. As a nonlimiting example, in a medical imaging context, PCA may be applied to a dataset pertaining to a 3D heart model, and accordingly, one might find that the first few eigenvalues are large, indicating that the main variations in heart shape may be described with a few principal components. These eigenvalues may be helpful in understanding major modes of shape variation, such as changes in size or specific anatomical features, which may be crucial for diagnosis and treatment planning. With continued reference to FIG.1A processor 102 may be configured to determine at least a medical feature 148 as a function of pose of anatomical structure. Medical feature 148 may include any type of structural, biological, physiological, or biomedical features or functions related to an anatomical structure of a patient and / or pertinent to concurrent medical procedure. Exemplary medical features 148 may include, without limitation, a case of calcification at a heart valve, a thrombus in the left atrial appendage, one or more abnormal heart valve movements or functions, and a thickness including a heart wall thickness, among others. For the purposes of this disclosure, “calcification” is a condition in which calcium deposits form on a heart valve. As 45 Attorney Docket No.1518-164PCT1 a nonlimiting example, a human heart contains four heart valves: the mitral valve located between the left atrium and the left ventricle, the aortic valve located between the left ventricle and the aorta, the tricuspid valve located between the right atrium and the right ventricle, and the pulmonary valve located between the right atrium and the pulmonary artery; calcification may occur at any of these locations with various likelihoods. These calcium deposits may cause the opening of a heart valve to become narrow. As a nonlimiting example, severe narrowing in the aortic valve may reduce the blood flow therethrough to result in a condition called aortic valve stenosis. In some cases, medical feature 148 may include one or more numerical or descriptive indicators that indicate a severity of calcification, such as “6 / 10” or “severe”. With continued reference to FIG.1A for the purposes of this disclosure, a “thrombus in the left atrial appendage” is a blood clot in a small, ear-shaped outpouching of the muscular wall of the left atrium. Such a blood clot may expose a patient to an increased risk for cerebral stroke or peripheral embolism and may be treated using a left atrial appendage occlusion procedure. For the purposes of this disclosure, an “abnormal” heart valve movement or function is a type of heart valve movement or function associated with a minority of population and / or described by a numerical value that is different from a statistical average of the population, according to one or more cutoffs and / or pre-determined criteria. As a nonlimiting example, an abnormal heart valve function may be specified as a heart valve function possessed by or associated with less than 10% of the population and / or described by a numerical value that is at least two standard deviations away from statistical average. Exemplary abnormal heart valve functions may include cases such as regurgitation (i.e., a case with a backward flow of blood due to a heart valve not closing properly), stenosis (i.e., a case where a heart valve’s leaflets thicken, stiffen, or stick together), and atresia (i.e., a case with one or more heart valves missing). For the purposes of this disclosure, a “heart valve movement” or “heart valve function” is a movement or function performed by a heart valve in order to regulate a blood flow. Exemplary heart valve movements or functions may include a direction towards which a heart valve may open, an extent to which a heart valve may open or close, among others, consistent with details described above. In some cases, medical feature 148 may include one or more numerical or descriptive indicators that indicate a general assessment of a heart valve movement or function and / or a severity of one or more abnormal heart valve movements or functions, such as “95 / 100” or “mild”. With continued reference to FIG.1A for the purposes of this disclosure, a “thickness” is a 46 Attorney Docket No.1518-164PCT1 radial distance, from an interior surface of an organ or tissue to an exterior surface of the organ or tissue, that measures the thickness of the structure in between. For the purposes of this disclosure, a “heart wall thickness” is a thickness pertaining to the muscle of a heart wall. Thickness such as heart wall thickness may vary from one individual to another, and different locations of an anatomical structure may have different thicknesses. Thickness may be a relevant parameter to factor in while performing concurrent medical procedure. As a nonlimiting example, for a cardiac ablation procedure, a lower power, a smaller amount of energy, and / or a shorter duration of operation may be applied to one part of a heart wall with a smaller heart wall thickness, whereas a higher power, a larger amount of energy, and / or a longer duration of operation may be applied to another part of a heart wall with a larger heart wall thickness. With continued reference to FIG.1A determining at least a medical feature 148 may include receiving feature training data including a plurality of training images as input and a plurality of labelled features as output. For the purposes of this disclosure, a “training image” is an image that may be used to train a machine learning algorithm or model. In one or more embodiments, feature training data may include data specifically synthesized for training purposes using one or more generative models, as described in this disclosure. In one or more embodiments, one or more query images may be incorporated into feature training data upon validation by users such as medical professionals. In one or more embodiments, feature training data may be retrieved from one or more databases, EHRs, and / or other repositories of similar nature, or be supplied as one or more user inputs. In one or more embodiments, at least a portion of feature training data may be added, deleted, replaced, or otherwise updated as a function of one or more inputs from one or more users. With continued reference to FIG.1A in one or more embodiments, plurality of labelled features may include a presence or absence of calcification at one or more heart valves. In one or more embodiments, plurality of labelled features may include a presence or absence of thrombus in the left atrial appendage of a heart. In one or more embodiments, the plurality of labelled features may include a plurality of heart valve functions. In one or more embodiments, plurality of labelled features may include a plurality of thicknesses such as heart wall thicknesses. With continued reference to FIG.1A determining at least a medical feature 148 may further include training an image processing machine learning model including a plurality of image processing algorithms using feature training data. For the purposes of this disclosure, an 47 Attorney Docket No.1518-164PCT1 “image processing machine learning model” is a data structure capable of processing at least a query image and / or training image and extracting one or more features therefrom. In some cases, image processing machine learning model may include a generative model and / or otherwise implement one or more types of artificial intelligence (AI) algorithms in consistence with any type of machine learning model or algorithm described herein that’s deemed suitable by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. For the purposes of this disclosure, a “data structure” is a format of data organization, management, and storage that is usually chosen for efficient access to data. Image processing machine learning model may include any type of data structure recognized by a person of ordinary skill in the art upon reviewing the entirety of this disclosure, such as without limitation, stack, queue, array, list, or tree. With continued reference to FIG.1A determining at least a medical feature 148 may further include determining at least a medical feature 148 as a function of at least a query image using the trained image processing machine learning model. It is worth noting that plurality of image processing algorithms may adopt a hierarchical structure and / or be implemented across a variety of functions of apparatus 100. In one or more embodiments, identifying pose of the heart may include locating at least a POV as a function of query image and a first subset of plurality of image processing algorithms, consistent with details described above. In such embodiments, determining at least a medical feature 148 may include determining the at least a medical feature 148 as a function of a second subset of plurality of image processing algorithms. As a nonlimiting example, the first subset of plurality of image processing algorithms may be trained using at least a portion of feature training data that contain a plurality of poses, POVs, and / or VA correlated with a collection of ICEs. Accordingly, the first subset of plurality of image processing algorithms, once trained, may use query image (e.g., a query ICE) to identify a match from this collection of ICEs and identify a set of pose, POV, and VA associated with the match. Identification of such a match may involve, for example and without limitation, comparing a first set of neural network encodings extracted from query image with at least a second set of neural network encodings extracted from each ICE of the collection of ICEs. For the purposes of this disclosure, “neural network encodings” are a plurality of parameters extracted by one or more neural networks that collectively describe features of a system and / or connections between elements therein. Neural network encodings may include weights / biases / coefficients of neural 48 Attorney Docket No.1518-164PCT1 network nodes, embeddings (vectors) generated by the neural networks, or a combination thereof, consistent with details described below in this disclosure. With continued reference to FIG.1A in one or more embodiments, determining at least a medical feature 148 may include identifying a thrombus in the left atrial appendage of the heart, consistent with details described above. In one or more embodiments, determining at least a medical feature may include identifying case of calcification in a heart valve consistent with details described above. In one or more embodiments wherein plurality of labelled features includes plurality of heart valve functions, determining at least a medical feature 148 may include classifying feature training data into a plurality of classes, using a classifier including any suitable type of classifier described in this disclosure, as a function of the plurality of heart valve functions, wherein each class of the plurality of classes shares at least one similar heart valve function, and classifying an abnormal heart valve function (e.g., based on its severity) as a function of the plurality of classes, consistent with details described in the rest of this disclosure. As a nonlimiting example, an abnormal heart valve function may be classified as “mild”, “intermediate”, or “severe.” With continued reference to FIG.1A in one or more embodiments wherein plurality of labelled features includes plurality of thicknesses, such as a plurality of heart wall thicknesses. Accordingly, determining at least a medical feature 148 may include determining thickness such as heart wall thickness. In such cases, determining at least a medical feature 148 may include labeling, using a labeling algorithm, feature training data with a plurality of labels as a function of plurality of thicknesses, wherein each label of the plurality of labels shares a similar thickness, and determining thickness as a function of the plurality of labels. For the purposes of this disclosure, a “labeling algorithm” is a mathematical algorithm or model configured to perform one or more labeling functions or creating one or more labels. For the purposes of this disclosure, “labeling” is a process of identifying raw data such as feature training data and adding one or more meaningful and informative labels to provide a context for one or more following steps. For the purposes of this disclosure, a “label” is an indication describing one or more characteristics of a subject matter (e.g., one or more medical features 148) as well as how the subject matter may be categorized into one or more categories with respect to a population or sub-population containing the subject. In one or more embodiments, label may include a binary label, e.g., “normal” vs. “abnormal” or “included” vs. “not included”. In one or more 49 Attorney Docket No.1518-164PCT1 embodiments, label may be further specified, such as “abnormally thick” or “abnormally thin”. In one or more embodiments, label may be associated with a percentile ranking, e.g., “top 10% of the population by heart wall thickness”. In one or more embodiments, label may be applied with respect to at least a specific cohort upon application of one or more inclusion / exclusion criteria, such as “top 25% of the female population by heart wall thickness” With continued reference to FIG.1A, labeling feature training data may include analyzing statistical distribution of thicknesses. For the purposes of this disclosure, a “statistical distribution” or “statistical model” is a mathematical model that describes a plurality of data elements collectively as a group. Statistical distribution may include one or more numerical indicators such as without limitation an average or mean, a median, a standard deviation, a variance, a range, or the like. Additionally, and / or alternatively, statistical distribution may include characteristics or metrics that describe how one data element or group of data elements compares to another data element or group of data elements. In some cases, a match between a first set of statistical metrics and a second set of statistical metrics may be determined as a function of one or more pre-determined criteria selected by one or more medical professionals. As a nonlimiting example, a first set of statistical metrics and a second set of statistical metrics may be considered a match when the two sets of statistical metrics are within one standard deviation from each other. As another nonlimiting example, matching a first set of statistical metrics with a second set of statistical metrics may involve a fuzzy set comparison, as described below. As another nonlimiting example, determining the heart wall thickness may include determining the heart wall thickness as an outlier based on statistical distribution, as described above. With continued reference to FIG.1A, it should be noted that apparatus 100 and methods described herein are not limited to medical or cardiac applications only. For example, and without limitation, visualization capabilities disclosed herein may be effectively adapted for use within other organs, such as brain, where precision and minimally invasive diagnostics are also crucial. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will recognize one or more embodiments described herein (although principally focused on the heart) and their underlaying principles may be readily transferrable to a broader spectrum of medical imaging and intervention applications such as, without limitation, transcatheter intervention (which is rapidly supplanting traditional open surgery), or other nonmedical contexts that are not 50 Attorney Docket No.1518-164PCT1 currently disclosed. Referring now to FIG.1B, an exemplary embodiment of a workflow 100b for apparatus 100 is illustrated. In one or more embodiments, generating electro-anatomical mapping 122 may involve a stepwise approach. Specifically, generating electro-anatomical mapping 122 may include generating an anatomical mapping 152 and an electrical mapping 154. For the purposes of this disclosure, an “anatomical mapping” is a mapping of anatomical structure of a tissue or an organ. In some cases, anatomical mapping 152 may be generated using an in vivo imaging system (IVIS) 156. For the purposes of this disclosure, an “electrical mapping” is a mapping of electrical activity of a tissue or an organ. Electrical mapping 154 may be generated using any suitable means recognized by a person of ordinary skill in the art upon reviewing the entirety of this disclosure, such as without limitation using a mapping catheter 158 and / or a mapping system 160. With continued reference to FIG.1B, generating an anatomical mapping 152 may include generating a 3D data structure. This 3D data structure may represent cardiac anatomy and may be generated as a function of set of images. In a non-limiting example, 3D data structure may include a 3D voxel occupancy representation (VOR). As used in this disclosure, a "3D voxel occupancy representation (VOR)" of a cardiac anatomy is a 3D digital representation of a spatial structure of the cardiac anatomy, 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 cardiac anatomy. 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 to process. In an embodiment, and still referring to FIG.1B, each voxel of plurality of voxels within VOR may include one or more embedded values. As used herein, “embedded values” refers to 51 Attorney Docket No.1518-164PCT1 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 cardiac anatomy 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 ICE images or other imaging modalities used to generate data structure. In some cases, embedded values may be utilized, by processor, 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. Still referring to FIG.1B, in an embodiment, each voxel of plurality of voxels may include a presence indicator. As used in this disclosure, a “presence indicator” refers to a data element that indicates a presence or absence (i.e., occupancy) of cardiac tissue within that portion. In some cases, and without limitation, presence indicator may include an occupancy status as one of the embedded values described herein. Portion may include a specific location within 3D space where data structure is generated; for instance, and without limitation, a coordinate in 3D space represented in a tuple such as (x, y, z). In an embodiment, 3D VOR may provide a spatial framework that allows for the modeling and visualization of cardiac anatomy in 3D space. In some cases, 3D data structure may include a plurality of layers or slices (either horizontal [e.g., xy plane] or vertical [e.g., xz or yz plane depends on the view direction]), wherein each layer or slices of the plurality of layers or slices is corresponding to a different cross-sectional view of subject’s heart, and collectively forming a comprehensive 3D depiction of the cardiac structure. In a non-limiting example, 3D VOR having plurality of voxels with presence indicators may indicate whether each voxel in 3D space may be occupied by a part of subject’s heart. A binary value such as 0 or 1 may be configured as presence indicator to show ether a pixel of 3D space is occupied (e.g., 1) or empty (e.g., 0). In should be noted that other values may be used as presence indicator such as a Boolean value e.g., TRUE or FALSE. In some cases, and still reference to FIG.1B, one or more embedded values, such as, without limitations, occupancy, or density, may be derived from set of images described herein by processor. In a non-limiting example, determining occupancy status of each voxel of plurality 52 Attorney Docket No.1518-164PCT1 of voxels may include converting set of ICE images 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, for each voxel as a function of the echogenicity of one or more pixels on a given ICE image, wherein, the brightness of the given ICE image may be analyzed since different tissues reflect ultrasound waves differently. With continued reference to FIG.1B, generating 3D data structure of cardiac anatomy may include generating a 3D array. In some cases, processor 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 ICE images of set of images. Additionally, or alternatively, and still referring to FIG.1B, 3D data structure of cardiac anatomy 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 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. Mapping presence indicators or other embedded values may include assigning each presence indicator or embedded value to each points within 3D grid such as corners of each corresponding cell. Such values may be derived from set of images as described above. In yet another embodiment, and still referring to FIG.1B, 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 an embodiment, processor may use interpolation to estimate other (unknown) embedded values within a range based on existing values such as known embedded 53 Attorney Docket No.1518-164PCT1 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.1B, 3D data structure of cardiac anatomy 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 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.1B, in some cases, presence indicators and / or other 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 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 may use mask to isolate the LA within the heart 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 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 gird 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 to highlight, exclude, or otherwise manipulate specific parts of cardiac anatomy within 3D grid. Processor 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 54 Attorney Docket No.1518-164PCT1 or other specific value (i.e., excluded or masked out). With continued reference to FIG.1B, in some embodiments, 3D grid may include one or more spatial features extracted from set of images of cardiac anatomy. 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.1B, 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 spatial features. 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 example, a vector represented as [5, 10, 15] may be treated as 55 Attorney Docket No.1518-164PCT1 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 lengthattribute l as derived using a Pythagorean norm: ^^ ൌ ^∑^ ଶ^ୀ^ ^^^ , where ai is attribute number i ofthe vector. Scaling and / or normalization may vector comparison independent of absolute quantities of attributes, while preserving any on similarity of attributes. Still referring to FIG.1B, 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 cardiac structures), such as curvature, surface area, volume, and / or the like. In another non-limiting example, one or more spatial features may include one or more texture features (i.e., characteristics related to the texture or pattern within cardiac tissues, as seen set of images), 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 cardiac 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 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 spatial features extracted from set of images in consistent with this disclosure. With continued reference to FIG.1B, in some embodiments, apparatus 100 may include a computer vision model configured to generate 3D data structure of cardiac anatomy. In an embodiment, computer vision model may process set of images, to make a determination about a scene, space, and / or object in cardiac anatomy. In a non-limiting example, computer vision 56 Attorney Docket No.1518-164PCT1 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 ICE image relative a 3D coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration of ICE 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 ICE image; however, a third dimension of registration, representing depth and / or a 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., ICE catheter), 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.1B, processor may use a machine learning module to implement one or more algorithms or generate one or more machine learning models, such as a cardiac anatomy modeling model to generate data structure of cardiac anatomy. 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. Still referring to FIG.1B, machine learning module may be used to generate cardiac 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 anatomy 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 data structure of cardiac anatomy includes receiving cardiac anatomy training data, wherein the cardiac anatomy training data may include a plurality of image sets as input and a plurality of computed tomography (CT) based cardiac anatomy models as output, and wherein each image set of plurality of image sets may include any images described in this disclosure. In some cases, cardiac anatomy training data may be 57 Attorney Docket No.1518-164PCT1 received from Image database or other databases. In other cases, cardiac 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. Still referring to FIG, 1, as used in this disclosure, a “computed tomography (CT) based cardiac anatomy model” refers to a 3D representation of the heart 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 cardiac anatomy model may include 3D representations of the heart including chambers, valves, blood vessels, and surrounding tissues. In some cases, CT-based cardiac anatomy model may be interactive; for instance, medical professionals may rotate, zoom, and / or explore CT- based cardiac anatomy model from various angles. In some cases, plurality of CT-based cardiac anatomy models may be generated prior to the training of the cardiac anatomy modeling model. Plurality of CT based cardiac anatomy models may be generated using existing techniques in the field as described above such as, without limitation, FAM, cardiac CT merging, among others. In a non-limiting example, plurality of CT-based cardiac anatomy models may provide ground through or references models against cardiac anatomy modeling model that is being trained. In a non-limiting example, generating data structure of cardiac anatomy further includes training cardiac anatomy modeling model using cardiac anatomy training data described herein. Cardiac anatomy modeling model trained using cardiac anatomy training data may be able to interpret ICE images by learning relationships between ICE images and corresponding CT-based cardiac anatomy models. Processor is further configured to generate data structure of cardiac anatomy as a function of set of images using trained cardiac anatomy modeling model. In some cases, data structure e.g., 3D VOR may be interpreted, visualized, and analyzed by processor in similar manner to CT-based cardiac anatomy models, wherein both are 3D structures that correspond to ICE images. With continued reference to FIG.1B, in an embodiment, cardiac anatomy modeling model comprises 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. In a non-limiting example, cardiac anatomy modeling model 58 Attorney Docket No.1518-164PCT1 may include a convolutional neural network (CNN). Generating 3D data structure of cardiac anatomy may include training CNN using cardiac anatomy training data and generating 3D data structure as a function of set of images using trained CNN. A “convolutional neural network,” for the purpose of 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. 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., set of images through a sliding window approach. In some cases, convolution operations may enable processor to detect local / global patterns, edges, textures, and any other spatial features described herein within each ICE image of set of images. Spatial features 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 data structure of cardiac anatomy. 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 spatial feature maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more spatial features. Still referring to FIG.1B, CNN may further include one or more fully connected layers configured to combine spatial features extracted by the convolutional and pooling layers as described above. 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 data structure of cardiac anatomy. 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.1B, CNN may further include a 3D CNN, wherein the 59 Attorney Docket No.1518-164PCT1 3D 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 to generate 3D structures such as 3D data structure of cardiac anatomy 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 set of images 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 ICE images while preserving spatial features as described above. Additionally, or alternatively, an encoder-decoder structure may be implemented (extended to 3D), by processor, 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 data structure of cardiac anatomy. With continued reference to FIG.1B, in an embodiment, training the cardiac anatomy 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 cardiac anatomy 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 cardiac anatomy modeling model’s parameters to minimize such loss. In a further non- limiting embodiment, instead of directly predicting 3D data structure, cardiac anatomy modeling model may be trained as a regression model to predict presence indicators and / or other 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 cardiac anatomy modeling. With continued reference to FIG.1B, alternatively, processor may generate a set of shape parameters based on set of images. As used in this disclosure, a “set of shape parameters” refers to a collection of numerical values or descriptors that quantitatively represent the geometric or 60 Attorney Docket No.1518-164PCT1 morphological characteristics of a structure e.g., a heart. In a non-limiting example, set of shape parameters may include information and / or metadata calculated, determined, and / or extracted from set of ICE images, such as, dimensions, angles, curvatures, surface areas, texture, symmetry, and / or the like. In other embodiments, processor may be configured to parameterize features (e.g., edges, textures, contours, and any other characteristics that describe the shape cardiac anatomy) extracted from set of images using CNN described herein. Such parameterization may involve processor to derive one or more shape parameters including one or more morphological descriptors that quantitatively describe cardiac anatomy based on extracted features. In some cases, processor may be configured to use principal component analysis (PCA) to reduce the dimensionality of set of shape parameters, allowing processor to focusing on the most informative shape parameters of set of shape parameters in further processing steps described below. With continued reference to FIG.1B, in a non-limiting example, set of shape parameters may be generated based on set of images using machine learning model such as, without limitation, a shape identification model. Generating set of shape parameters may include receiving cardiac geometry training data, wherein the cardiac geometry training data may include a plurality of image sets as input correlated to a plurality of shape parameter sets as output. In some cases, cardiac geometry training data may be received from Image database described herein. For example, and without limitation, cardiac geometry training data may be used to show each ICE image may indicate a particular set of shape parameters. Shape identification model may be trained, by processor, using cardiac geometry training data. Additionally, cardiac geometry training data may include previously input image sets and their corresponding shape parameters output. Shape identification model may be iterative such that outputs may be used as future inputs of shape identification model. This may allow the shape identification model to evolve. Processor may be further configured to generate set of shape parameters as a function of set of images using the trained shape identification model. With continued reference to FIG.1B, processor may be configured to generate an initial 3D model of cardiac anatomy. As used in this disclosure, an “initial 3D model” is a foundational representation, capturing the basic geometric and spatial characteristics of the heart in 3D space. In an embodiment, initial 3D model may provide a “starting point” for further refinement and customization as described in further detail below, allowing for the incorporation of more 61 Attorney Docket No.1518-164PCT1 detailed and patient-specific information. In some cases, initial 3D model may be generated through a direct 3D reconstruction from a series of (2D) ICE images. In a non-limiting example, set of images may include a plurality of ICE images captured from different angles and positions within the heart. Processor 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., initial model of cardiac anatomy. In some cases, such direct 3D reconstruction may leverage the inherent spatial information within set of images, providing a direct and intuitive way to model the initial model of the heart's structure. In a further embodiment, generic 3D modeling techniques may be applied to create the initial 3D model. In some cases, generic 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 may be used by processor to generate initial 3D model of cardiac anatomy. Additionally, or alternatively, and still referring to FIG.1B, initial 3D model may be generated based on a plurality of standard anatomical templates, wherein the “plurality of standard anatomical templates,” for the purpose of this disclosure, refers to predefined and commonly accepted representations of the human body’s anatomical structures. In some cases, plurality of standard anatomical templates may be selected from Image database as described herein based on statistical averages or shared characteristics. In a non-limiting example, initial 3D model may include a template model selected from a plurality of pre-determined template models. Plurality of pre-determined template model may be generated by processor based on plurality of standard anatomical templates prior to the generation of initial 3D model using 3D reconstruction / modeling algorithms / techniques as listed above. In an embodiment, generating initial 3D model may include selecting template model from plurality of template models based on set of ICE images. In some cases, template model may represent a typical or average cardiac anatomy that is most similar to cardiac anatomy pertaining to subject. Such similarity may be determined based on one or more similarity metrics, such as without limitation, structural similarity index (SSI), MSE, peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), Pearson correlation coefficient, and / or the like between set of images and each image sets stored in Image database. Template model may be adjusted and customized to fit the specific 62 Attorney Docket No.1518-164PCT1 patient's ICE images as described below in further detail. With continued reference to FIG.1B, processor may be configured to refine generated initial 3D model of cardiac anatomy as a function of 3D data structure of cardiac anatomy. In a non-limiting embodiment, refining initial 3D model of cardiac anatomy may include utilizing a statistical shape model (SSM). It should be noted that SSM may not be the only method for refining initial 3D model. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various methods, such as, without limitation, mesh smoothing techniques, level set method, physics-based simulation, among others may be implemented, by processor 102, to refine initial 3D model described herein. As used in this disclosure, a “statistical shape model (SSM)” is 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. In some cases, SSM may be constructed by analyzing one or more datasets of shapes and identifying, for example, mean shapes and main modes of variation within the one or more datasets. In a non-limiting example, SSM may start with calculation of at least one mean shape, which represents an average geometry of all the heart shapes in a given dataset, wherein the at least one mean shape may be served as a central reference point for processor to understand different variations. In some cases, dataset may include, without limitation, cardiac anatomy training data, cardiac geometry training data, and / or any datasets within ICE image databases described herein. SSM may also identify one or more principal modes of variation within given datasets described herein, wherein the “principal modes of variations,” for the purpose of this disclosure, refer to main patterns or directions along which data points vary within dataset. In a non limiting example, identifying principal modes of variations may include applying principal component analysis (PCA) on given dataset. Additionally, or alternatively, shapes may be described directly using plurality of shape parameter sets (in cardiac geometry training data). In some cases, shape parameter sets may correspond to a plurality of modes of variations. Further, one or more statistical constraints (e.g., mean, variance, correlation, boundary, proportion constraint and / or the like) may be introduced into SSM based on the distribution of shape parameters within plurality of shape parameter sets. With continued reference to FIG.1B, refining initial 3D model of cardiac anatomy may include aligning initial 3D model with 3D VOR of cardiac anatomy. In an embodiment, aligning initial 3D model with 3D VOR may include matching template model to 3D VOR; for instance, 63 Attorney Docket No.1518-164PCT1 and without limitation, this may involve adjusting the position, orientation, and scale of template model to match the spatial distribution captured in 3D VOR. In some cases, matching template model to 3D VOR may include matching spatial features, wherein matching the spatial features may further include aligning the surface, boundaries and internal structures of template model with corresponding features in 3D VOR. In some embodiments, processor may utilize one or more optimization techniques to achieve a desired alignment; for instance, and without limitation, processor may be configured to minimizing the difference between template model and 3D VOR using iterative closest point (ICP) algorithms, gradient descent, or any other optimization strategies. Additionally, alignment of template model with 3D VOR may also allow incorporation of patient-specific details (e.g., patient profile) into initial 3D model to form a final model as described in further detail below. In a non-limiting example, and still referring to FIG.1B, refining initial 3D model of cardiac anatomy may include deforming, using processor, template model to match 3D data structure of cardiac anatomy. As used in this disclosure, “deforming” means altering the geometric structure of a structure e.g., template model in a systematic and controlled manner to align the structure with the spatial characteristics captured in another structure e.g., 3D VOR. In some cases, processor 102 may utilize one or more mathematical deformation models such as, without limitation, B-splines, radial basis functions, or other deformation functions to control and guide the deformation process of template model. In some cases, one or more constraints listed above may be applied, by processor, based on anatomical knowledge, biomechanical properties, or other relevant factors to ensure that the deformation of template model is realistic and consistent with physiological principles as would be understood and / or expected by an ordinary person skilled in the art. Still referring to FIG.1B, additionally, or alternatively, refining initial 3D model of cardiac anatomy may also include validating template model or deformed template model against 3D data structure or additional data such as, without limitation, expert input, adjust parameters, and / or the like. Such validation process may ensure that the refined model accurately represents the underlaying cardiac anatomy. In some cases, expert input may include any user input entered via a user interface as described in further detail below. In a non-limiting example, expert input may include, without limitation, clinical assessment, anatomical knowledge, or other professional insights that guide and evaluate the refinement process inputted to apparatus 100 by 64 Attorney Docket No.1518-164PCT1 one or more users including medical professionals, subjects, patients, and / or any other related individuals. In a further embodiment, validating template model or deformed template model against 3D data structure may also include fine-tuning defamation controls, alignment settings, or other model characteristics or properties to achieve desired alignment with 3D VOR or additional data. In some cases, other information that is incorporated and codified within template model / deformed template model and / or 3D data structure such as medical imaging, biomechanical simulations, patient-specific data / metadata may be validated and cross-verified. At least a machine-learning process, for example a machine-learning model described herein, may be used to validate by processor. Processor may use any machine-learning process described in this disclosure for this or any other functions. With continued reference to FIG.1B, in some embodiments, embedded values described herein may be employed in the refinement process of initial 3D model of cardiac anatomy. In a non-limiting example, the embedded values may contribute to SSM by providing additional parameters that guide the deformation and alignment of the template to match 3D VOR. Embedded values such as, without limitation, presence indicators may be used by processor to guide the deformation process by providing targets for alignment; for instance, and without limitation, SSM may be configured to identify specific target areas where initial 3D model e.g., a 3D LA model that needs to be deformed. Presence indicators, in this case, may reveal a bulge in LA wall that is not present in initial 3D model. In some cases, presence indicators may define the exact shape of the bulge in LA wall. Processor 102 may then deform initial 3D model, particularly the wall to match the bulge defined by presence indicators in 3D VOR. With continued reference to FIG.1B, processor may be configured to generate a subsequent 3D model of cardiac anatomy as a function of the refinement. As used in this disclosure, a “subsequent 3D model” refers to a more detailed and accurate 3D representation of cardiac anatomy. In an embodiment, subsequent 3D model may be derived from initial 3D model and / or template model and adjusted based on 3D data structure as described above. In such embodiment, subsequent 3D model may include a deformed initial 3D model and / or template model. In a non-limiting example, 3D VOR may indicate a need of adjustment to initial 3D model of left ventricle to match subject’s unique geometry. SSM may then be configured to generate subsequent 3D model that accurately captures such specific cardiac anatomy based on initial 3D model and 3D VOR. In other cases, initial 3D model may not need any refinement; for 65 Attorney Docket No.1518-164PCT1 instance, and without limitation, if initial 3D model already align perfectly with 3D data structure representing subject’s right atrium (RA), no deformation or adjustment would be necessary, thereby resulting in subsequent 3D model that is identical to initial 3D model. Still referring to FIG.1B, in some cases, the refinement process may also include the incorporation of more detailed features and textures based on 3D data structure and embedded values thereof, enhancing the realism and specificity of initial 3D model. In an embodiment, SSM may be integrated with one or more additional models such as, without limitation, texture models, appearance models, or functional models to generate subsequent 3D model. In some cases, such integration may result in subsequent 3D model that reflects not just the geometry but also the biomechanical properties or blood flow dynamics within cardiac anatomy. In a non limiting example, texture of the myocardium may be modeled, by integrating texture models with SSM, to represent the fibrous nature of the heart muscle. In another non-limiting example, appearance of blood vessels, including color variations and translucency, may be modeled, by integrating appearance models with SSM. With continued reference to FIG.1B, alternatively, refining initial 3D model of cardiac anatomy may include adjusting template model based on set of shape parameters. In an embodiment, processor may be configured to map set of shape parameters to SSM. The mapping process may define how template model should be adjusted to represent specific subject’s cardiac anatomy. In a non-limiting example, shape parameters may include one or more numeric values indicating a particular thick ventricular wall, processor may configure SSM to adjust template model to reflect such characteristic. In an embodiment, generating subsequent 3D model may involve generating a 3D mesh or grid that accurately represents the shape defined by set of shape parameters; for instance, and without limitation, processor may be configured to generate a 3D mesh for left ventricle with vertices and edges positioned according to specific curvature and thickness defined by set of shape parameters using SSM. With continued reference to FIG.1B, in some embodiments, processor may be configured to input subsequent 3D model back into cardiac anatomy modeling model and / or shape identification model for continuous learning. In some cases, training data for these models such as, without limitation, cardiac anatomy training data, cardiac geometry training data, and / or the like may be updated, by replacing, appending or otherwise inserting subsequent 3D model (and corresponding set of ICE images) into the dataset. This iterative process may allow machine 66 Attorney Docket No.1518-164PCT1 learning module to evolve over time, adapting to new set of ICE images and improving the accuracy of machine learning models generated by machine learning module. Incorporation of subsequent 3D models as additional training data may enable apparatus 100 to capture more variations and nuances in cardiac anatomy modeling, enhancing its ability to generalize across different patients and conditions. Still referring to FIG.1B, additionally, processor may use user feedback to train the machine-learning models described above. For example, cardiac anatomy modeling model and / or shape identification model may be trained using past inputs and outputs of cardiac anatomy modeling model and / or shape identification model. In some embodiments, if user feedback indicates that a subsequent 3D model outputted by SSM was “bad,” then that output and the corresponding input e.g., set of ICE images, corresponding CT-based cardiac anatomy model, and / or template model, may be removed from training data used to train cardiac anatomy modeling model and / or shape identification model, and / or may be replaced with a value entered by, e.g., another user that represents an ideal 3D model of the heart given the input the machine learning models originally received, permitting use in retraining, and adding to training data as described above; in either case, machine learning models described herein may be retrained with modified training data. In some embodiments, training data such as cardiac anatomy training data and / or cardiac geometry training data may include user feedback. Further, apparatus 100 may be configured to validate one or more machine learning models described herein against real world data, identifying areas where machine learning models may be underperforming or misaligned with clinical needs. Such feedback may also be used to guide model training, ensuring that machine learning models are not only accurate but also clinically meaningful and aligned with healthcare or medical professional’s needs and priorities. With continued reference to FIG.1B, in one or more embodiments, generating electro- anatomical mapping 122 may include aligning electrical mapping 154 with anatomical mapping 152 using fiducial point-based registration 162. For the purposes of this disclosure, “fiducial point-based registration” is a method used in image processing and computer vision to align or map two or more data sets into a common coordinate system. This technique may involve selecting distinct and easily identifiable points, known as fiducial points, in each data set. These points may serve as references or landmarks. By applying mathematical transformations, such as scaling, rotation, and translation, among others, datasets may be adjusted so that their 67 Attorney Docket No.1518-164PCT1 corresponding fiducial points overlap as closely as possible. This process may be crucial in applications such as medical imaging, where accurate alignment of images is necessary for diagnosis and treatment planning. Referring now to FIG.2, in one or more embodiments, an exemplary embodiment 200 of an electrocardiogram (ECG) is illustrated. ECG may include a plurality of features such as P- wave, Q-wave, R-wave, S-wave, QRS complex, and T wave, as well as a plurality of parameters such a PR interval 204, QT interval 208, ST interval 212, TP interval 216, RR interval 220, and the like. P-wave may reflect atrial depolarization (activation). For the purposes of this disclosure, a “PR interval” is the distance between the onset of P-wave to the onset of QRS complex. PR interval 204 may be assessed to determine whether impulse conduction from the atria to the ventricles is normal. PR interval 204 may be measured in seconds. For the purposes of this disclosure, a “QT interval” is a reflection of the total duration of ventricular depolarization and repolarization and is measured from the onset of QRS complex to the end of T-wave. The QT duration may be inversely related to heart rate; i.e., QT interval 208 may increase at slower heart rates and decrease at higher heart rates. Therefore, to determine whether QT interval 208 is within normal limits, it may be necessary to adjust for the heart rate. A heart rate-adjusted QT interval 208 is referred to as a corrected QT interval 208 (QTc interval). A long QTc interval may indicate an increased risk of ventricular arrhythmias. The QTc interval may be in the range of 0.36 to 0.44 seconds. For the purposes of this disclosure, an “RR interval” is the time between two consecutive R waves. For the purposes of this disclosure, a “QRS complex” is a representation of the depolarization (activation) of ventricles depicted between Q-, R- and S- waves, although it may not always display all three waves. Since the electrical vector generated by the left ventricle is usually many times larger than the vector generated by the right ventricle, QRS complex is a reflection of left ventricular depolarization. With continued reference to FIG.2, for the purposes of this disclosure, an “ST interval” is the segment of ECG that starts at the end of QRS complex and extends to the beginning of T wave; it represents the early part of ventricular repolarization. ST segment may be relatively isoelectric, meaning it is at the baseline, with minimal elevation or depression. The normal duration of ST interval 212 is usually around 0.12 seconds. For the purposes of this disclosure, a “TP interval” is the segment of ECG that extends from the end of T wave to the beginning of the next P wave; it represents the time when the ventricles are fully repolarized and are in a resting 68 Attorney Docket No.1518-164PCT1 state. The duration of TP interval 216 may vary but is typically short, as it may represent the brief pause between cardiac cycles. Significant deviations may be associated with certain conditions affecting repolarization. For the purposes of this disclosure, an “RR interval” is the time between two consecutive R waves of ECG; it may represent the duration of one cardiac cycle, encompassing both atrial and ventricular depolarization and repolarization. RR interval 220 may be measured in seconds and can be used to calculate heart rate (beats per minute) usingℎ^^^^^^^^ ^^^^^^^^ ൌ ^^ோோ ூ^௧^^௩^^ (in seconds). The intervals described above may be used to determine a ventricular rate, i.e., the number of ventricular contractions (heartbeats) that occur in one minute, which may be closely related to RR interval 220 of ECG, as the RR interval 220 represents the time between two consecutive ventricular contractions. Referring now to FIG.3, an exemplary embodiment of an intracardiac echocardiogram (ICE) 300 is illustrated. As described above, input data 106 and / or at least a medical image 110 may include ultrasound data, such as without limitation an ICE 300. In one or more embodiments, ICE 300 may provide a real-time, dynamic a view 304 of the heart’s interior structures, including, without limitation, right atrium (RA) 308, anterior descending (AD) 312, pulmonary atresia (PA) 316, and right ventricular (RV) 320. With continued reference to FIG.3, in one or more embodiments, ICE 300 may include a grayscale image. It should be noted that, in one or more embodiments, ICE 300 may be configured to visualize blood flow and / or blood flow patterns within heart via color doppler. In one or more embodiments, resolution and / or clarity of ICE 300 may be superior to transthoracic or transesophageal echocardiography due to that ICE catheter may be positioned inside heart, closer to the structures being imaged. With continued reference to FIG.3, in a nonlimiting example, heart chambers may appear as dark, anechoic (black) areas since they are filled with blood, which doesn’t reflect ultrasound waves well. Heart walls, valves, and / or other structures may appear as varying shades of gray, depending on their density and composition. In one or more embodiments, color doppler overlays may show blood flow in different colors, indicating the direction and speed of blood flow. For instance, and without limitation, red may indicate flow towards the probe, whereas blue may indicate flow away from the probe. With continued reference to FIG.3, in one or more embodiments, ICE 300 may be synchronized with ECG data as described above, allowing for precise timing of cardiac events 69 Attorney Docket No.1518-164PCT1 with anatomical visualization provided by ICE 300. In one or more embodiments, ICE 300 may include an ECG display 324 configured to display ECG waveform as a continuous line graph at the top, bottom, or side of the ICE 300. In one or more embodiments, specific parts of the cardiac cycle, e.g., systole or diastole, may be correlated and / or synchronized with visual data from ICE 300. With continued reference to FIG.3, additionally or alternatively, ICE 300 may be accompanied by metadata 328 displayed on the side or corners of ICE 300 as described herein. In one or more embodiments, metadata 328 may provide essential contextual information about ICE 300 and / or the corresponding patient. In a nonlimiting example, metadata 328 may include patient information (e.g., patient ID, name, DOB, age, gender, and the like), image acquisition details (e.g., date and time, probe type, frequency, depth, gain, and the like), procedure-related information (e.g., procedure name, operator, location, and the like), ECG trace, measurement annotations (e.g., any measurements taken directly on the image e.g., diameter, a value of thickness of a heart wall, and the like), image sequence information (e.g., image number, total number of frames, and the like), comments or notes, hospital or clinic information, and / or the like. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of how ICE 300 and various components thereof may be incorporated within apparatus 100 to generate electro-anatomical mapping 122. Referring now to FIG.4, an exemplary embodiment of GUI 400 is illustrated. GUI 400 shows an exemplary session of apparatus 100 wherein concurrent medical procedure 416 includes ablation procedure. GUI 400 displays query image 408 that contains ICE. GUI 400 also summarizes the findings of apparatus 100 by displaying medical features 448 including a detected case of calcification at the mitral valve, a detected case of a thin heart wall, as well as including possible abnormal valve functions and calcification at the aortic valve that are not currently detected. For the purposes of this disclosure, a “user interface” is a means by which a user and a computer system interact, for example, using input devices and software. User interface 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, or the like. In one or more embodiments, a user may interact with user interface using computing device distinct from and communicatively connected to processor, such as a smartphone, tablet, or the like operated by the user. User interface may include one or 70 Attorney Docket No.1518-164PCT1 more graphical locator and / or cursor facilities allowing user to interact with graphical models and / or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and / or other manual data entry device. For the purposes of this disclosure, a “graphical user interface (GUI)” is a type of user interface that allows end users to interact with electronic devices through visual representations. In one or more embodiments, GUI may include icons, menus, other visual indicators or representations (graphics), audio indicators such as primary notation, display information, and related user controls. 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 as a pull-down menu. Menu may include a context menu that appears only when user performs a specific action. Files, programs, web pages, and the like may be represented using a small picture within GUI. In one or more embodiments, GUI may include a graphical visualization of a user profile and / or the like. In one or more embodiments, processor may be configured to modify and / or update GUI as a function of at least an input or the like by populating a user interface data structure and visually presenting data through modification of the GUI. With continued reference to FIG.4, in one or more embodiments, GUI may contain one or more interactive elements. For the purposes of this disclosure, an “interactive element” is an element within GUI that allows for communication with processor by one or more users. For example, and without limitation, interactive elements may include a plurality of tabs wherein selection of a particular tab, such as for example, by using a fingertip, may indicate to a system to perform a particular function and display the result through GUI. In one or more embodiments, interactive element may include tabs within GUI, wherein the selection of a particular tab may result in a particular function. In one or more embodiments, interactive elements may include words, phrases, illustrations, and the like to indicate a particular process that one or more users would like system to perform. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which user interfaces, GUIs, and / or elements thereof may be implemented and / or used as described in this disclosure. With continued reference to FIG.4, in one or more embodiments, display device 428 and / or remote device may be configured to display at least an event handler graphic corresponding to at least an event handler. For the purposes of this disclosure, an “event handler graphic” is a graphical element with which user may interact using display device 428 and / or 71 Attorney Docket No.1518-164PCT1 remote device to enter data, for instance and without limitation, one or more user inputs. Event handler graphic may include, without limitation, a button, a link, a checkbox, a text entry box and / or window, a drop-down list, a slider, or any other event handler graphic deemed suitable by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. For the purposes of this disclosure, an “event handler” is a module, data structure, function, and / or routine that performs an action on display device 428 and / or remote device in response to one or more user inputs. For instance, and without limitation, event handler may record data corresponding to user selections of previously populated fields such as drop-down lists and / or text auto-complete and / or default entries, data corresponding to user selections of checkboxes, radio buttons, or the like, potentially along with automatically entered data triggered by such selections, user entry of textual data using a keyboard, touchscreen, speech-to-text program, or the like. Event handler may generate prompts for further information, may compare data to validation rules such as requirements that the data in question be entered within certain numerical ranges, and / or may modify data and / or generate warnings to user in response to such requirements. Event handler may convert data into expected and / or desired formats, for instance such as date formats, currency entry formats, name formats, or the like. Event handler may transmit data from a remote device to computing device. With continued reference to FIG.4, in one or more embodiments, event handler may include a cross-session state variable. For the purposes of this disclosure, a “cross-session state variable” is a variable recording data entered on remote device during a previous session. Such data may include, for instance, previously entered text, previous selections of one or more elements as described above, or the like. For instance, cross-session state variable data may represent a search that user entered in a past session. Cross-session state variable may be saved using any suitable combination of client-side data storage on remote device and server-side data storage on computing device; for instance, data may be saved wholly or in part as a “cookie” which may include data or an identification of remote device to prompt provision of cross- session state variable by the computing device, which may store the data on the computing device. Alternatively, or additionally, computing device may use login credentials, device identifier, and / or device fingerprint data to retrieve cross-session state variable, which the computing device may transmit to remote device. Cross-session state variable may include at least a prior session datum. A prior session datum may include any element of data that may be 72 Attorney Docket No.1518-164PCT1 stored in cross-session state variable. Event handler graphic may be further configured to display at least a prior session datum, for instance and without limitation, by auto-populating user query data from previous sessions. With continued reference to FIG.4, in one or more embodiments, processor and / or computing device may configure display device 428 and / or remote device to generate a graphical view. For the purposes of this disclosure, a “graphical view” is a data structure that results in display of one or more graphical elements on a screen. Graphical view may include at least a display element. For the purposes of this disclosure, a “display element” is an image that a program and / or data structure may cause to be displayed. Display elements may include, without limitation, windows, pop-up boxes, web browser pages, display layers, and / or any other display element deemed relevant by a person of ordinary skill in the art upon reviewing the entirety of this disclosure. Graphical view may include at least a selectable event graphic corresponding to one or more selectable event handlers. For the purposes of this disclosure, a “selectable event graphic” is a graphical element that, upon selection using a cursor or other locator as manipulated using a locator device such as a mouse, touchscreen, track pad, joystick, or the like, will trigger an action to be performed. As a nonlimiting example, a selectable event graphic may include a redirection link, defined as a hyperlink, button, image, portion of an image, and / or other graphic containing or referring to a uniform resource locator (URL) and / or other resource locator to another graphical view including without limitation buttons, and / or to a process that performs navigation to such URL and / or other resource locator upon selection of selectable event graphic. Redirection may be performed using any event handler, including without limitation event handlers detecting the click of a mouse or other locator, access of redirection link using a touchscreen, the selection of any key, mouseover events, or the like. In further reference to FIG.4, graphical view may suggest, using display device 428, a modification 496a-d to concurrent medical procedure 416 as a function of the at least a medical feature. In some cases, such modification 496a-d may be automated or semi-automated, under an autopilot or a semi-autopilot mode, that requires limited human intervention. In one or more embodiments wherein determining at least a medical feature includes identifying a case of calcification in a heart valve, suggesting modification 496a to concurrent medical procedure 416 may include avoiding, using catheter, the heart valve upon identifying the case of calcification. In one or more embodiments wherein determining at least a medical feature includes identifying 73 Attorney Docket No.1518-164PCT1 a thrombus in the left atrial appendage of the heart, as described above, suggesting modification 496b-c to concurrent medical procedure 416 may include terminating or adjusting ablation procedure upon identifying the thrombus and / or abnormal heart valve function. In one or more embodiments, suggesting modification 496d to concurrent medical procedure 416 may include adjusting at least a parameter of ablation procedure as a function of thickness as described above. Exemplary parameters of ablation procedure may include without limitation voltage, pulse duration, frequency, pulse width, amplitude, power of ablation, total energy delivered, total treatment time, energy delivered to a particular location, treatment time at a particular location, current, average power, peak power, and biphasic vs monophasic pulse delivery, among others. Now referring to FIG.5, a flow diagram of an exemplary embodiment of an ICE example generation process 500. In an embodiment, cardiac anatomy training data may be generated, at least in part, via ICE example generation process 500. In some cases, processor 102 may be configured to receive a 3D model of the heart, such as, without limitation, template model, initial model, subsequent 3D model, and / or any 3D model of cardiac anatomy 516 as described herein and identify an ICE view 504 (i.e., visual representation of image obtained using intracardiac echocardiography as described above e.g., ICE image) based on the received 3D model. In some cases, 3D model received by processor 102 may be derived from CT scans as described above. In other cases, processor may receive CT scans directly instead of 3D models. A synthetic ICE frame 508 may then be generated, by processor 102, as a function of identified ICE view 504, wherein the synthetic ICE frame may be used as one or the training examples in cardiac anatomy training data. With continued reference to FIG.5, in some cases, processor 102 may interface with one or more 3D models (i.e., detailed representation of heart’s anatomy in a 3D space, capturing intricate structures, chambers, vessels, valves, among others) as described above, or other imaging modalities and / or databases, and equipped with algorithms e.g., CNN, gradient boosting machines, SVM, PCA, and / or the like to analyze model’s geometry and spatial relationships upon receiving the 3D models. In some cases, 3D models may be received from SSM 580 as described above via a communicative connection between processor 102 and SSM 580. In a non- limiting example, processor 102 may be configured to determine an optimal viewpoints or angles from which ICE view 504 would provide a desired diagnostic value or procedural guidance. Still referring to FIG.5, in some cases, identification and selection of ICE view 504 may 74 Attorney Docket No.1518-164PCT1 be automatically identified, using one or more machine learning models as described herein. In a non-limiting example, processor 102 may utilize one or more machine learning models trained on cardiac anatomy viewpoints identification training data, wherein the cardiac anatomy viewpoints identification training data may include a plurality of cardiac anatomies as input correlated to a plurality of ICE images as output and identify at least one ICE view 504 (most informative) for a given cardiac anatomy using the trained machine learning models. Still referring to FIG.5, in other cases, ICE view 504 may be defined by a user such as a medical professional. In a non-limiting example user interface of display device may allow a user (e.g., a clinician) to manually rotate, pan, and zoom displayed 3D model and / or corresponding CT scans. As user do so, processor 102 may dynamically calculate and displays potential ICE views 504 based on user’s chosen perspective. Additionally, or alternatively, depending on cardiac procedure being planned or executed, processor 102 may prioritize certain ICE views 504. For instance, and without limitation, ICE view 504 may be pre-defined. For atrial fibrillation ablation, ICE view 504 may showcase the pulmonary veins’ entrances into the LA may be emphasized. In other cases, ICE view 504 may be automatically identified, by processor 102, using one or more machine learning models as described herein, such as, without limitation, synthetic ICE data generator as described in detail below. With continued reference to FIG.5, as used in this disclosure, a “synthetic ICE frame” refers to a digitally generated or simulated image that emulates a visual representation obtained from ICE view 504. In some cases, synthetic ICE frames 508 may be produced using computational methods and / or models such as, without limitation, a synthetic ICE data generator 512 based on pre-existing data, models, or simulations e.g., identified ICE views 504. In a non- limiting example, synthetic ICE frames 508 may include a simplified version e.g., an image illustrating heart anatomy via a plurality of lines indicating contours of heart’s structure as shown in FIG.5. One or more image processing techniques and / or computer vision algorithms such as, without limitation, histogram equalization, adaptive filtering, edge detection (e.g., Canny or Sobel operators), contour extraction, and / or the like may be applied, by processor 102, on a segmented CT scan and / or 3D models based on identified ICE view 504. Synthetic ICE frame 508 may be rendered on a blank canvas or background that mimics the echogenicity of an ICE image according to extracted contours, wherein the extracted contours may be represented as a bold lines and enhanced with shading to give depth. In some cases, synthetic ICE frame 508 may 75 Attorney Docket No.1518-164PCT1 be validated and verified by overlaying synthetic ICE frame 508 onto original ICE view 504, ensuring accuracy and resemblance. Still referring to FIG.5, in some cases, generating synthetic ICE frames 508 may include implementations of one or more aspects of “generative artificial intelligence(AI),” a type of AI that uses machine learning algorithms to create, establish, or otherwise generate data such as, without limitation, ICE images, ICE videos, and / or the like that is similar to one or more provided training examples. In an embodiment, machine learning module described herein may generate one or more generative machine learning models that are trained on one or more set of CT scans and / or 3D models in ICE image view 504 as described above. Synthetic ICE data generator 512 may include one or more generative machine learning models may be configured to generate new examples that are similar to the training data of the one or more generative machine learning models but are not exact replicas; for instance, and without limitation, data quality or attributes of the generated examples may bear a resemblance to the training data provided to one or more generative machine learning models, wherein the resemblance may pertain to underlying patterns, features, or structures found within the provided training data. Still referring to FIG.5, in some cases, generative machine learning models within synthetic ICE data generator may include one or more generative models. As described herein, “generative models” refers to statistical models of the joint probability distribution ^^^^^,^^^ on a given observable variable x, representing features or data that can be directly measured or observed (e.g. CT scans and / or 3D models derived from CT scans) and target variable y, representing the outcomes or labels that one or more generative models aims to predict or generate (e.g., synthetic ICE frames 508). In some cases, generative models may rely on Bayes theorem to find joint probability; for instance, and without limitation, Naïve Bayes classifiers may be employed by computing device to categorize input data such as, without limitation, CT scans and / or 3D models derived from CT scans into different views. In a non-limiting example, and still referring to FIG.5, one or more generative machine learning models may include one or more Naïve Bayes classifiers generated, by processor 102, 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 76 Attorney Docket No.1518-164PCT1 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 generated by first transforming training data into a frequency table. Processor 102 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Processor 102 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. Still referring to FIG.5, although Naïve Bayes classifier may be primarily known as a probabilistic classification algorithm; however, it may also be considered a generative model described herein due to its capability of modeling the joint probability distribution ^^^^^,^^^ over observable variables X and target variable Y. In an embodiment, Naïve Bayes classifier may be configured to make an assumption that the features X are conditionally independent given classlabel Y, allowing generative model to estimate the joint distribution as ^^^^^, ^^^ ൌ ^^^^^^∏^^^^^^^^^ ∣^^^, wherein ^^^^^^ may be the prior probability of the class, and ^^^^^^|^^^ is the conditional probability of each feature given the class. One or more generative machine learning models containing Naïve Bayes classifiers may be trained on labeled training data, estimating conditional probabilities ^^^^^^|^^^ and prior probabilities ^^^^^^ for each class; for instance, and without limitation, using techniques such as Maximum Likelihood Estimation (MLE). One or more generative machine learning models containing Naïve Bayes classifiers may select a class label ^^ according to prior distribution ^^^^^^, and for each feature ^^^, sample at least a value according to conditional distribution ^^^^^^|^^^. Sampled feature values may then be combined to form one or more new data instance with selected class label ^^. In a non-limiting example, one or more generative machine learning models may include one or more Naïve Bayes classifiers to generate new examples of ICE images based on CT scans and / or 3D models derived from CT scans (e.g., identified ICE views 504), wherein the models may be trained using training data containing a plurality of features of input data as described herein and / or the like correlated to a plurality of ICE views. Still referring to FIG.5, in some cases, one or more generative machine learning models 77 Attorney Docket No.1518-164PCT1 may include generative adversarial network (GAN). As used in this disclosure, a “generative adversarial network” is a type of artificial neural network with at least two sub models (e.g., neural networks), a generator, and a discriminator, that compete against each other in a process that ultimately results in the generator learning to generate new data samples, wherein the “generator” is a component of the GAN that learns to create hypothetical data by incorporating feedbacks from the “discriminator” configured to distinguish real data from the hypothetical data. In some cases, generator may learn to make discriminator classify its output as real. In an embodiment, discriminator may include a supervised machine learning model while generator may include an unsupervised machine learning model as described in further detail below. With continued reference to FIG.5, in an embodiment, discriminator may include one ormore discriminative models, i.e., models of conditional probability ^^^^^|^^ ൌ ^^^ of targetvariable Y, given observed variable X. In an embodiment, discriminative models may learn boundaries between classes or labels in given training data. In a non-limiting example, discriminator may include one or more classifiers as described in further detail below with reference to FIG.5 to distinguish between different categories e.g., real vs. fake, or states e.g., TRUE vs. FALSE within the context of generated data such as, without limitations, synthetic ICE frames 508, and / or the like. In some cases, processor 102 may implement one or more classification algorithms such as, without limitation, Support Vector Machines (SVM), Logistic Regression, Decision Trees, and / or the like to define decision boundaries. In a non-limiting example, and still referring to FIG.5, generator of GAN may be responsible for creating synthetic data that resembles real ICE images. In some cases, GAN may be configured to receive CT scans and / or 3D models derived from CT scans as input and generates corresponding examples of ICE images containing information describing heart anatomy in different ICE views. On the other hand, discriminator of GAN may evaluate the authenticity of the generated content by comparing it to true ICE images, for example, discriminator may distinguish between genuine and generated content and providing feedback to generator to improve the model performance. Additionally, or alternatively, GAN may include a conditional GAN as an extension of the basic GAN as described herein that allows for generation of ICE images using pre-existing CT scans and / or 3D models derived from CT scans based on certain conditions or labels. In standard GAN, generator may produce samples from random noise, while in a conditional GAN, generator may produce samples based on random noise and a 78 Attorney Docket No.1518-164PCT1 given condition or label. With continued reference to FIG.5, in other embodiments, one or more generative models may also include a variational autoencoder (VAE). As used in this disclosure, a “variational autoencoder” is an autoencoder (i.e., an artificial neural network architecture) whose encoding distribution is regularized during the model training process in order to ensure that its latent space includes desired properties allowing new data sample generation. In an embodiment, VAE may include a prior and noise distribution respectively, trained using expectation- maximization meta-algorithms such as, without limitation, probabilistic PCA, sparse coding, among others. In a non-limiting example, VEA may use a neural network as an amortized approach to jointly optimize across input data and output a plurality of parameters for corresponding variational distribution as it maps from a known input space to a low-dimensional latent space. Additionally, or alternatively, VAE may include a second neural network, for example, and without limitation, a decoder, wherein the “decoder” is configured to map from the latent space to the input space. In a non-limiting example, and still referring to FIG.5, VAE may be used by processor 102 to model complex relationships between CT scans and / or 3D models derived from CT scans. In some cases, VAE may encode input data into a latent space, capturing example ICE images. Such encoding process may include learning one or more probabilistic mappings from observed CT scans and / or 3D models derived from CT scans to a lower-dimensional latent representation. Latent representation may then be decoded back into the original data space, therefore reconstructing the 3D models representing example ICE images. In some cases, such decoding process may allow VAE to generate new examples or variations that are consistent with the learned distributions. Additionally, or alternatively, and still referring to FIG.5, processor 102 may be configured to continuously monitor synthetic ICE data generator. In an embodiment, processor 102 may configure discriminator to provide ongoing feedback and further corrections as needed to subsequent input data. An iterative feedback loop may be created as processor 102 continuously receive real-time data, identify errors (e.g., distance between synthetic ICE frame 508 and real ICE images) as a function of real-time data, delivering corrections based on the identified errors, and monitoring subsequent model outputs and / or user feedbacks on the delivered corrections. In an embodiment, processor 102 may be configured to retrain one or more 79 Attorney Docket No.1518-164PCT1 generative machine learning models within synthetic ICE data generator based on user modified ICE frames or update training data of one or more generative machine learning models within synthetic ICE data generator by integrating validated synthetic ICE frames (i.e., subsequent model output) into the original training data. In such embodiment, iterative feedback loop may allow synthetic ICE data generator to adapt to the user’s needs and performance requirements, enabling one or more generative machine learning models described herein to learn and update based on user responses and generated feedbacks. With continued reference to FIG.5, other exemplary embodiments of generative machine learning models may include, without limitation, long short-term memory networks (LSTMs), (generative pre-trained) transformer (GPT) models, mixture density networks (MDN), and / or the like. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine learning models may be used generating synthetic ICE frames 508. Still referring to FIG.5, in a further non-limiting embodiment, synthetic ICE data generator 512 may be further configured to generate a multi-model neural network that combines various neural network architectures described herein. In a non-limiting example, multi-model neural network may combine LSTM for time-series analysis with GPT models for natural language processing. Such fusion may be applied by computing device to generate synthetic ICE frames 508. In some cases, multi-model neural network may also include a hierarchical multi- model neural network, wherein the hierarchical multi-model neural network may involve a plurality of layers of integration; for instance, and without limitation, different models may be combined at various stages of the network. Convolutional neural network (CNN) may be used for image feature extraction, followed by LSTMs for sequential pattern recognition, and a MDN at the end for probabilistic modeling. Other exemplary embodiments of multi-model neural network may include, without limitation, ensemble-based multi-model neural network, cross- modal fusion, adaptive multi-model network, among others. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various generative machine learning models may be used to generating synthetic ICE frames 508 as described herein. As an ordinary person skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various multi-model neural network and combination thereof that may be implemented by apparatus 100 in consistent with this disclosure. 80 Attorney Docket No.1518-164PCT1 Now referring to FIG.6, an exemplary embodiment of a 3D VOR 600 is illustrated.3D VOR 600 may be used to represent 3D object 604. In an embodiment, 3D VOR 600 may divide a 3D space 608 into a grid of one or more cubic units e.g., voxels 612, wherein each voxel 612 represents a specific volume within 3D space 608. In a non-limiting example, 3D object 604 may include a cardiac anatomy pertaining to a subject. Still referring to FIG.6, in some cases, each voxel 612 may act as a basic building block. In a non-limiting example, each voxel 612 may be configured to represent a discrete portion of 3D space 608. In an embodiment, each voxel 612 may include a presence indicator as described above, which denotes whether the voxel is occupied or unoccupied. In such embodiment, the binary or continuous value may allow 3D VOR 600 to map the presence or absence of material within each voxel 612, creating a granular representation of 3D object 604. With continued reference to FIG.6, in some cases, the resolution of 3D VOR 600 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.6, in an embodiment, voxels 612 may be arranged in a regular pattern along three axis 616a-b, each pointing a distinct direction. In a non-limiting example, voxels 612 may be arranged along x, y, and z axes, wherein such arrange may facilitate efficient manipulation and rendering of the 3D object 604. In some cases, spatial features 620a-c such as, without limitation, edges, surfaces, textures, and any other spatial features as described above, may be extracted from 3D VOR 600 by analyzing the relationships and patterns between neighboring voxels. Now referring to FIG.7, an exemplary embodiment of an overlaid heat map 700 is illustrated. Heat map 700 may include elements described with reference to other figures and / or may be generated as described with reference to other figures. Heat map 700 may illustrate one or more of levels of uncertainty differentiated by color, shading, texture, and the like as described above. For example, heat map 700 may depict a first level of uncertainty 704. In some embodiments, first level of uncertainty 704 may be determined as a function of a level of uncertainty associated with one or more points within a region associated with first level of uncertainty, such as point 708. In some embodiments, heat map 700 may depict one or more additional levels of uncertainty, such as second level of uncertainty 712, third level of 81 Attorney Docket No.1518-164PCT1 uncertainty 716, and / or fourth level of uncertainty 720. In some embodiments, levels of uncertainty may be displayed as discrete regions and / or discrete levels of uncertainty. This may make heat map 700 more readable for a user than an alternative in which continuous levels of uncertainty are displayed. In some embodiments, levels of uncertainty may be displayed on a continuous scale. For example, each point on heat map 700 may have a color associated with its level of uncertainty. This may improve accuracy of depictions of uncertainty at specific locations. In some embodiments, one or more levels of uncertainty may represent a certain percentage of certainty and / or accuracy, in the depiction of a shape parameter, location, geometric identifier and the like. In some embodiments, map 700 may depict levels of uncertainty relative to other levels of uncertainty of map 700. For example, if first level of uncertainty 704 is the highest level of uncertainty of map 700, and second level of uncertainty 712 is the lowest level of uncertainty of map 700, then third level of uncertainty 716 and / or fourth level of uncertainty 720 may have values, colors, patterns, levels of transparencies, and the like associated with how close to the minimum and maximum levels of uncertainty they are. In some embodiments, each level of uncertainty may be measured on an absolute scale. For example, first level of uncertainty 704 may have a value, color, pattern, level of transparency, and the like associated with a percent likelihood that a 3D model at first level of uncertainty 704 is correct. In some embodiments, each level of uncertainty may be scaled based on color code / texture code based scales as described above. For example, first level of uncertainty 704 may include a light shading of an area of the heart model, wherein as the level of uncertainty progress, shading darkens in second level of uncertainty 712. Referring now to FIG.8, an exemplary system 800 for generating a three-dimensional (3D) model of cardiac anatomy 816 is illustrated. System 800 may include cardiac image capture device 804. Cardiac image capture device 804 may include a cardiac image capture device described with reference to another figure herein. For example, cardiac image capture device 804 may include an ICE catheter. System 800 may further include computing device 808. Computing device may receive a first set of images, such as a first set of ICE images, from cardiac image capture device 804. Computing device 808 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 82 Attorney Docket No.1518-164PCT1 map and / or a 3D model to a user using user interface 872 and / or user device 868. User 812 may receive information, such as information as to a level of uncertainty at a particular location of a 3D model and / or cardiac anatomy 816 and may position cardiac image capture device 804 within cardiac anatomy 816 in order to capture a second set of images, such as a second set of ICE images. User 812 may perform this through, for example, interaction with a user interface 872. The 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 812 through the user interface 872. Referring now to FIG.9, an exemplary embodiment of a machine learning module 900 that may perform one or more machine learning processes as described above 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. For the purposes of this disclosure, a “machine learning process” is an automated process that uses training data 904 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 908 given data provided as inputs 912. This is in contrast to a non-machine learning software program where the commands to be executed are pre-determined by a user and written in a programming language. With continued reference to FIG.9, “training data”, for the purposes of this disclosure, are data containing correlations that a machine learning process uses to model relationships between two or more categories of data elements. For instance, and without limitation, training data 904 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 904 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 904 according to various correlations; correlations may indicate causative and / or predictive links 83 Attorney Docket No.1518-164PCT1 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 904 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 nonlimiting example, training data 904 may include data entered in standardized forms by persons or processes, such that entry of a given data element within a given field in a given form may be mapped to one or more descriptors of categories. Elements in training data 904 may be linked to descriptors of categories by tags, tokens, or other data elements. For instance, and without limitation, training data 904 may be provided in fixed-length formats, formats linking positions of data to categories 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. With continued reference to FIG.9, alternatively or additionally, training data 904 may include one or more elements that are uncategorized; that is, training data 904 may not be formatted or contain descriptors for some elements of data. Machine learning algorithms and / or other processes may sort training data 904 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 nonlimiting 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 904 to be made applicable for two or more distinct machine learning algorithms as described in further detail below. Training data 904 used by machine learning module 900 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a nonlimiting illustrative example, inputs may include plurality of exemplary 84 Attorney Docket No.1518-164PCT1 medical images and plurality of exemplary electrograms, whereas outputs may include plurality of exemplary electro-anatomical mappings. With continued reference to FIG.9, training data 904 may be filtered, sorted, and / or selected using one or more supervised and / or unsupervised machine learning processes and / or models as described in further detail below; such processes and / or models may include without limitation a training data classifier 916. For the purposes of this disclosure, a “classifier” is a machine learning model that sorts inputs into categories or bins of data, outputting the categories or bins of data and / or labels associated therewith. Machine learning model may include without limitation a data structure representing and / or using a mathematical model, neural net, or a program generated by a machine learning algorithm, known as a “classification algorithm”. 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 900 may generate a classifier using a classification algorithm. For the purposes of this disclosure, a “classification algorithm” is a process wherein a computing device and / or any module and / or component operating therein derives a classifier from training data 904. 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. In one or more embodiments, training data classifier 916 may classify elements of training data to a plurality of cohorts as a function of certain anatomic traits. With continued reference to FIG.9, machine learning module 900 may be configured to generate a classifier using a naive Bayes classification algorithm. Naive 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. Naive 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. NaiveBayes classification algorithm may be based on Bayes Theorem expressed as ^^^^^ / ^^^ ൌ^^^^^ / ^^^ ൈ ^^^^^^ ൊ ^^^^^^, where ^^^^^ / ^^^ is the probability of hypothesis ^^ given data ^^, also85 Attorney Docket No.1518-164PCT1 known as posterior probability; ^^^^^ / ^^^ is the probability of data ^^ given that the hypothesis ^^ was true; ^^^^^^ is the probability of hypothesis ^^ being true regardless of data, also known as prior probability of ^^; and ^^^^^^ is the probability of the data regardless of the hypothesis. A naive Bayes algorithm may be generated by first transforming training data into a frequency table. Machine learning module 900 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Machine learning module 900 may utilize a naive Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naive Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naive Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naive Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary. With continued reference to FIG.9, machine learning module 900 may be configured to generate a classifier using a k-nearest neighbors (KNN) algorithm. For the purposes of this disclosure, a “k-nearest neighbors algorithm” is or at least includes a classification method that utilizes feature similarity to analyze how closely out-of-sample features resemble training data 904 and to classify input data to one or more clusters and / or categories of features as represented in training data 904. This may be performed by representing both training data 904 and input data in vector forms and using one or more measures of vector similarity to identify classifications within training data 904 and 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 of training data 904 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 nonlimiting example, an initial heuristic may include a ranking of associations between inputs 912 and elements of training data 904. Heuristic may include selecting some number of highest- ranking associations and / or training data elements. With continued reference to FIG.9, generating k-nearest neighbors algorithm may 86 Attorney Docket No.1518-164PCT1 generate a first vector output containing a data entry cluster, generating a second vector output containing 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 2. 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 when their directions and / or relative quantities of values are the same; thus, as a nonlimiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for the 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 length attribute l as derived using aPythagorean norm: ^^ ൌ ^∑^^ୀ^ ^^^ଶ , where ^^^ is attribute number of vector ^^. Scaling and / ornormalization may 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 904 are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values. With continued reference to FIG.9, 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 904 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 model and / or process that may exist in a range of 87 Attorney Docket No.1518-164PCT1 values in a population of phenomena such as images, user data, process data, physical data, or the like, a computing device, processor 102, and / or machine learning module 900 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 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 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 102, and / or machine learning module 900 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 user, another device, or the like. With continued reference to FIG.9, computing device, processor 102, and / or machine learning module 900 may be configured to preprocess training data 904. For the purposes of this disclosure, “preprocessing” training data is a process that transforms training data from a 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. With continued reference to FIG.9, computing device, processor 102, and / or machine learning module 900 may be configured to sanitize training data. For the purposes of this disclosure, “sanitizing” training data is a process whereby training examples that interfere with convergence of a machine learning model and / or process are removed to yield 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 skewed to an unlikely range of input 912 and / or output 908; 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” means having a signal-to-noise ratio below a threshold value. In one or more embodiments, sanitizing 88 Attorney Docket No.1518-164PCT1 training data may include steps such as removing duplicative or otherwise redundant data, interpolating missing data, correcting data errors, standardizing data, identifying outliers, and / or the like. In one or more embodiments, sanitizing training data may include algorithms that identify duplicate entries or spell-check algorithms. With continued reference to FIG.9, in one or more embodiments, images used to train an image classifier or other machine learning model and / or process that takes images as inputs 912 or generates images as outputs 908 may be rejected if image quality is below a threshold value. For instance, and without limitation, computing device, processor 102, and / or machine learning module 900 may perform blur detection. Elimination of one or more blurs may be performed, as a nonlimiting example, by taking Fourier transform or a Fast Fourier Transform (FFT) of 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 nonlimiting 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 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 a wavelet-based operator, which uses coefficients of a discrete wavelet transform to describe the frequency and spatial content of images. Blur detection may be performed using statistics-based operators that 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. With continued reference to FIG.9, computing device, processor 102, and / or machine learning module 900 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 912 and / or outputs 908 requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more elements of training examples to be used as or compared to inputs 912 and / or outputs 908 may be modified to have such a number of units of 89 Attorney Docket No.1518-164PCT1 data. In one or more embodiments, computing device, processor 102, and / or machine learning module 900 may convert a smaller number of units, such as in a low pixel count image, into a desired number of units by upsampling and interpolating. As a nonlimiting example, a low pixel count image may have 100 pixels, whereas a desired number of pixels may be 128. Processor 102 may interpolate the low pixel count image to convert 100 pixels into 128 pixels. It should also be noted that one of ordinary skill in the art, upon reading the entirety of this disclosure, would recognize 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 one or more embodiments, a set of interpolation rules may be trained by sets of highly detailed inputs 912 and / or outputs 908 and corresponding inputs 912 and / or outputs 908 downsampled to smaller numbers of units, and a neural network or another machine learning model that is trained to predict interpolated pixel values using the training data 904. As a nonlimiting example, a sample input 912 and / or output 908, 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 nonlimiting 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, computing device, processor 102, and / or machine learning module 900 may utilize sample expander methods, a low-pass filter, or both. For the purposes of 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 102, and / or machine learning module 900 may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units. With continued reference to FIG.9, in one or more embodiments, computing device, processor 102, and / or machine learning module 900 may downsample elements of a training 90 Attorney Docket No.1518-164PCT1 example to a desired lower number of data elements. As a nonlimiting example, a high pixel count image may contain 256 pixels, however a desired number of pixels may be 128. Processor 102 may downsample the high pixel count image to convert 256 pixels into 128 pixels. In one or more embodiments, processor 102 may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nthentry in a sequence of samples, all but every Nthentry, 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 and / or anti-imaging filters, and / or low-pass filters, may be used to eliminate side effects of compression. With continued reference to FIG.9, feature selection may include narrowing and / or filtering training data 904 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, elements, or training data including such elements based on relevance to or utility for an intended task or purpose for which a 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.9, 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, wherein a difference between each value, ^^, and a minimum value, ^^^^^, in a set orsubset of values is divided by a range of values, ^^^^௫ െ ^^^^^, in the set or subset: ^^^^௪ ൌ^ି^^^^Feature scaling may include mean normalization, wherein a difference between each and a mean value of a set and / or subset of values, ^^^^^^, is divided by a range ofvalues, ^^ െ ^^ , in the set or subs^ି^^^ೌ^^^௫ ^^^ et: ^^^^௪ ൌ^. Feature scaling may include ^ೌ^ି^^^^ standardization, wherein a difference ^^ and ^^^^^^is divided by a standard deviation, ^^, of a set or subset of values: ^^^ି^^^ೌ^^^௪ൌ . Feature scaling may be performed using a median value of a set or subset, X , range (IQR), which represents 91 Attorney Docket No.1518-164PCT1 difference between the 25thpercentile value and the 50thpercentile value (or closest values thereto by a rounding protocol), such as: ^^^^௪ൌ^ି^^^^^ೌ^ூொோ . A Person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative or additional approaches that may be used for feature scaling. With continued reference to FIG.9, computing device, processor 102, and / or machine learning module 900 may be configured to perform one or more processes of data augmentation. For the purposes of this disclosure, “data augmentation” is a process that adds data to a training data 904 using elements and / or entries already in the dataset. Data augmentation may be accomplished, without limitation, using interpolation, generation of modified copies of existing entries and / or examples, and / or one or more generative artificial intelligence (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. With continued reference to FIG.9, machine learning module 900 may be configured to perform a lazy learning process and / or protocol 920. For the purposes of this disclosure, a “lazy learning” process and / or protocol is a process whereby machine learning is conducted upon receipt of input 912 to be converted to output 908 by combining the input 912 and training data 904 to derive the algorithm to be used to produce the output 908 on demand. A lazy learning process may alternatively be referred to as a “lazy loading” or “call-when-needed” process and / or protocol. For instance, an initial set of simulations may be performed to cover an initial heuristic and / or “first guess” at an output 908 and / or relationship. As a nonlimiting example, an initial heuristic may include a ranking of associations between inputs 912 and elements of training data 904. Heuristic may include selecting some number of highest-ranking associations and / or training data 904 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a k-nearest neighbors algorithm, a lazy naive Bayes algorithm, or the like. A person of ordinary skill 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. 92 Attorney Docket No.1518-164PCT1 With continued reference to FIG.9, alternatively, or additionally, machine learning processes as described in this disclosure may be used to generate machine learning models 924. A “machine learning model”, for the purposes of this disclosure, is a data structure representing and / or instantiating a mathematical and / or algorithmic representation of a relationship between inputs 912 and outputs 908, generated using any machine learning process including without limitation any process described above, and stored in memory. An input 912 is submitted to a machine learning model 924 once created, which generates an output 908 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 nonlimiting example, a machine learning model 924 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 by "training" the network, in which elements from a training data 904 are applied to the input nodes, and 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, as described in detail below. With continued reference to FIG.9, machine learning module 900 may perform at least a supervised machine learning process 928. For the purposes of this disclosure, a “supervised” machine learning process is a process with algorithms that receive training data 904 relating one or more inputs 912 to one or more outputs 908, and seek to generate one or more data structures representing and / or instantiating one or more mathematical relations relating input 912 to output 908, 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 inputs 912 described above as inputs, and outputs 908 described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs 912 and outputs 908. Scoring function may, for instance, seek to maximize the probability that a given input 912 and / or combination thereof is associated with a given output 908 to minimize the probability that a given input 912 is not associated with a given output 908. 93 Attorney Docket No.1518-164PCT1 Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs 912 to outputs 908, 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 904. Supervised machine learning processes may include classification algorithms as defined above. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine learning process 928 that may be used to determine a relation between inputs and outputs. With continued reference to FIG.9, training a supervised machine learning process may include, without limitation, iteratively updating coefficients, biases, and weights based on an error function, expected loss, and / or risk function. For instance, an output 908 generated by a supervised machine learning process 928 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. Updates 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 904 are exhausted and / or until a convergence test is passed. For the purposes of this disclosure, a “convergence test” is a test for a condition selected to indicate 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. With continued reference to FIG.9, a computing device, processor 102, and / or machine 94 Attorney Docket No.1518-164PCT1 learning module 900 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, computing device, processor 102, and / or machine learning module 900 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 908 of previous repetitions as inputs 912 to subsequent repetitions, aggregating inputs 912 and / or outputs 908 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 102, apparatus 100, or machine learning module 900 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. A person of ordinary skill 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.9, machine learning process may include at least an unsupervised machine learning process 932. For the purposes of this disclosure, an unsupervised machine learning process is a process that derives inferences in datasets without regard to labels. As a result, an unsupervised machine learning process 932 may be free to discover any structure, relationship, and / or correlation provided in the data. Unsupervised processes 932 may not require a response variable, may 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. With continued reference to FIG.9, machine learning module 900 may be designed and configured to create machine learning model 924 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 95 Attorney Docket No.1518-164PCT1 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 the square root of the sum of squares of all terms. Linear regression models may include an 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 a person of ordinary skill 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 a person of ordinary skill in the art upon reviewing the entirety of this disclosure. With continued reference to FIG.9, 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 naive 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 96 Attorney Docket No.1518-164PCT1 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. With continued reference to FIG.9, 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 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 912 and / or output 908 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 application-specific integrated circuits (ASICs), production and / or configuration of reconfigurable hardware elements, circuits, and / or modules such as without limitation field programmable gate arrays (FPGAs), production and / or configuration of non- reconfigurable and / or non-rewritable memory elements, circuits, and / or modules such as without limitation non-rewritable read-only memory (ROM), 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 912 from any other process, module, and / or component described in this disclosure, and produce outputs 908 to any other process, module, and / or component described in this disclosure. With continued reference to FIG.9, 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 97 Attorney Docket No.1518-164PCT1 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. 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 908 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 908 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. With continued reference to FIG.9, 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 904 may include, without limitation, training examples including inputs 912 and correlated outputs 908 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 908 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, 98 Attorney Docket No.1518-164PCT1 instructions, or the like. With continued reference to FIG.9, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 936. For the purposes of this disclosure, a “dedicated hardware unit” is a hardware component, circuit, or the like, aside from a principal control circuit and / or processor 102 performing method steps as described in this disclosure, that is specifically designated or selected to perform one or more specific tasks and / or processes described in reference to this figure. Such specific tasks and / or processes may include without limitation preprocessing and / or sanitization of training data and / or training a machine learning algorithm and / or model. Dedicated hardware unit 936 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, in parallel, and / or the like. Such dedicated hardware units 936 may include, without limitation, graphical processing units (GPUs), dedicated signal processing modules, field programmable gate arrays (FPGA), other reconfigurable hardware that has been configured to instantiate parallel processing units for one or more specific tasks, or the like. Computing device, processor 102, apparatus 100, or machine learning module 900 may be configured to instruct one or more dedicated hardware units 936 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, vector and / or matrix operations, and / or any other operations described in this disclosure. Referring now to FIG.10, an exemplary embodiment of neural network 1000 is illustrated. For the purposes of this disclosure, a neural network or 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 1004, at least an intermediate layer of nodes 1008, and an output layer of nodes 1012. Connections between nodes may be created via the process of "training" neural network 1000, in which 99 Attorney Docket No.1518-164PCT1 elements from a training dataset are applied to the input nodes, and 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 1000 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 nonlimiting example, neural network 1000 may include a convolutional neural network comprising an input layer of nodes 1004, one or more intermediate layers of nodes 1008, and an output layer of nodes 1012. For the purposes of this disclosure, a “convolutional neural network” is a type of neural network 1000 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.11, an exemplary embodiment of a node 1100 of neural network 1000 is illustrated. Node 1100 may include, without limitation, a plurality of inputs, ^^^, that may receive numerical values from inputs to neural network 1000 containing the node 1100 and / or from other nodes 1100. Node 1100 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 its equivalent, a linear activation function whereby an output is directly proportional to input, and / or a nonlinear activation function wherein the output is not proportional to the input. Nonlinearactivation functions may include, without limitation, a sigmoid function of the form ^^^^^^ ൌ^^ut ^^, a tanh (hyperbolic tangent) functionି^ష^given inp^^ି^ష^of the form^^ା^ష^, a tanh derivativefunction such as ^^^^^^ ൌ ^^^^^^ℎଶ^^^^, a rectified linear unit function such as ^^^^^^ ൌ ^^^^^^ ^0, ^^^, a“leaky” and / or “parametric” rectified linear unit function such as ^^^^^^ ൌ ^^^^^^ ^^^^^, ^^^ for somevalue of ^^, an exponential linear units function such as ^^^^^^ ൌ ^ ^^ ^^^^^^ ^^ ^ 0^^^^^௫ െ 1^ ^^^^^^ ^^ ^ 0for some value of ^^ (this function may be replaced and / or weighted by its own derivative in someembodiments), a softmax function such as ^^^^^^^ ൌ ^^∑^ ௫^where the inputs to an instant layer are^^^, a swish function such as ^^^^^^ ൌ ^^ ∗ ^^^^^^^^^^^^^^^^^^, a Gaussian error linear unit function such100 Attorney Docket No.1518-164PCT1as ^^^^^^ ൌ ^^൫1 ^ ^^^^^^ℎ ^^2 / ^^^^^ ^ ^^^^^^^൯ for some values of ^^, ^^, and ^^, and / or a scaled^^^^^௫ 1^ ^^^^^^ ^^ ^ 0. Fundamentally, there functions. As a sum of inputs using weights, ^^^, that are multiplied by respective inputs, ^^^,. Additionally, or alternatively, a bias ^^ may be added to the weighted sum of the inputs such that an offset is added to each unit in a neural network layer 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, ^^. Weight, ^^^, applied to an input, ^^^, may indicate whether the input is “excitatory”, indicating that it has strong influence on the one or more outputs, ^^, for instance by the corresponding weight having a large numerical value, or “inhibitory”, indicating it has a weak influence on the one more outputs, y, for instance by the corresponding weight having a small numerical value. The values of weights, ^^^, may be determined by training neural network 1000 using training data, which may be performed using any suitable process as described above. Referring now to FIG.12, an exemplary embodiment of a method 1200 for generating electro-anatomical mapping 122 is described. At step 1205, method 1200 includes receiving, by processor 102, input data 106, wherein receiving the input data includes receiving, from imaging device 108, at least a medical image 110 and receiving, from signal capturing device 116, at least an electrogram 118. This step may be implemented with reference to details described above in this disclosure and without limitation. With continued reference to FIG.12, at step 1210, method 1200 includes generating, by processor 102 using at least a machine learning model 124, electro-anatomical mapping 122 as a function of input data 106, wherein the at least a machine learning model 124 is trained using electro-anatomical mapping training data 126 including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output. This step may be implemented with reference to details described above in this disclosure and without limitation. With continued reference to FIG.12, at step 1215, method 1200 includes displaying, by processor 102 using user interface 138, electro-anatomical mapping 122. This step may be implemented with reference to details described above in this disclosure and without limitation. 101 Attorney Docket No.1518-164PCT1 Referring now to FIG.13, it is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to one of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and / or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and / or software module. Such software may be a computer program product that employs a machine-readable storage medium. A machine- readable storage medium may be any medium that is capable of storing and / or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and / or embodiments described herein. Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random-access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission. Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data- carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and / or embodiments described herein. Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of 102 Attorney Docket No.1518-164PCT1 executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and / or be included in a kiosk. With continued reference to FIG.13, the figure shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computing system 1300 within which a set of instructions for causing the computing system 1300 to perform any one or more of the aspects and / or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and / or methodologies of the present disclosure. Computing system 1300 may include a processor 1304 and a memory 1308 that communicate with each other, and with other components, via a bus 1312. Bus 1312 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. Processor 1304 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit, which may be regulated with a state machine and directed by operational inputs from memory and / or sensors; processor 1304 may be organized according to Von Neumann and / or Harvard architecture as a non-limiting example. Processor 1304 may include, incorporate, and / or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor, field programmable gate array, complex programmable logic device, graphical processing unit, general-purpose graphical processing unit, tensor processing unit, analog or mixed signal processor, trusted platform module, a floating-point unit, and / or system on a chip. With continued reference to FIG.13, memory 1308 may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input / output system 1316, including basic routines that help to transfer information between elements within computing system 1300, such as during start-up, may be stored in memory 1308. Memory 1308 (e.g., stored on one or more machine-readable media) may also include instructions (e.g., software) 1320 embodying any one or more of the aspects and / or methodologies of the present disclosure. In another example, memory 1308 may further include 103 Attorney Docket No.1518-164PCT1 any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof. With continued reference to FIG.13, computing system 1300 may also include a storage device 1324. Examples of a storage device (e.g., storage device 1324) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 1324 may be connected to bus 1312 by an appropriate interface (not shown). Example interfaces include, but are not limited to, small computer system interface, advanced technology attachment, serial advanced technology attachment, universal serial bus, IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 1324 (or one or more components thereof) may be removably interfaced with computing system 1300 (e.g., via an external port connector (not shown)). Particularly, storage device 1324 and an associated machine-readable medium 1328 may provide nonvolatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for computing system 1300. In one example, software 1320 may reside, completely or partially, within machine-readable medium 1328. In another example, software 1320 may reside, completely or partially, within processor 1304. With continued reference to FIG.13, computing system 1300 may also include an input device 1332. In one example, a user of computing system 1300 may enter commands and / or other information into computing system 1300 via input device 1332. Examples of input device 1332 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 1332 may be interfaced to bus 1312 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 1312, and any combinations thereof. Input device 1332 may include a touch screen interface that may be a part of or separate from display device 1336, discussed further below. Input device 1332 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above. With continued reference to FIG.13, user may also input commands and / or other 104 Attorney Docket No.1518-164PCT1 information to computing system 1300 via storage device 1324 (e.g., a removable disk drive, a flash drive, etc.) and / or network interface device 1340. A network interface device, such as network interface device 1340, may be utilized for connecting computing system 1300 to one or more of a variety of networks, such as network 1344, and one or more remote devices 1348 connected thereto. 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, such as network 1344, may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 1320, etc.) may be communicated to and / or from computing system 1300 via network interface device 1340. With continued reference to FIG.13, computing system 1300 may further include a video display adapter 1352 for communicating a displayable image to a display device, such as display device 1336. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 1352 and display device 1336 may be utilized in combination with processor 1304 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computing system 1300 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 1312 via a peripheral interface 1356. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof. The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a 105 Attorney Docket No.1518-164PCT1 multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and / or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention. Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention. 106 Attorney Docket No.1518-164PCT1

Claims

WHAT IS CLAIMED IS:

1. An apparatus for generating electro-anatomical mapping, wherein the apparatus comprises: a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to: receive input data, wherein receiving the input data comprises: receiving, from an imaging device, at least a medical image; and receiving, from a signal capturing device, at least an electrogram; generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, wherein the at least a machine learning model is trained using electro-anatomical mapping training data comprising exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output; and display the electro-anatomical mapping using a user interface, wherein generating the electro-anatomical mapping comprises: generating a putative electro-anatomical mapping; validating the putative electro-anatomical mapping using a plurality of quality assurance parameters; and creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation.

2. The apparatus of claim 1, wherein the at least a medical image comprises one or more of at least a computed tomography (CT) scan and at least a magnetic resonance imaging (MRI) scan.

3. The apparatus of claim 1, wherein the at least an electrogram comprises at least an electrocardiogram (ECG).

4. The apparatus of claim 1, wherein the input data further comprises one or more members from a group consisting of ultrasound data, point-of-care ultrasound (POCUS) data, intravascular ultrasound data, abdominal ultrasound data, at least a transthoracic echocardiogram (TTE), at least a transesophageal echocardiogram (TEE), at least an 107 Attorney Docket No.1518-164PCT1intracardiac echocardiogram (ICE), and at least a stress echocardiogram.

5. The apparatus of claim 1, wherein the processor is further configured to: identify at least a target location pertaining to a medical procedure within the electro- anatomical mapping; and highlight the at least a target location within the electro-anatomical mapping using the user interface.

6. The apparatus of claim 5, wherein: the medical procedure comprises an ablation procedure; and the electro-anatomical mapping is used as an initial mapping for the ablation procedure.

7. The apparatus of claim 6, wherein the processor is further configured to: receive, from a navigation system, a location of a catheter; and display, using the user interface, the location of the catheter on the electro-anatomical mapping.

8. The apparatus of claim 7, wherein the processor is further configured modify the electro- anatomical mapping as a function of the location of the catheter.

9. The apparatus of claim 5, wherein the processor is further configured to: identify at least a medical feature within the electro-anatomical mapping; and adjust at least a parameter pertaining to the medical procedure as a function of the at least a medical feature.

10. The apparatus of claim 1, wherein generating the electro-anatomical mapping comprises aligning an electrical mapping with an anatomical mapping using fiducial point-based registration.

11. The apparatus of claim 1, wherein the electro-anatomical mapping comprises a color- coded heat map.

12. The apparatus of claim 1, wherein: the exemplary medical images comprise historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures; the exemplary electrograms comprise historical electrograms pertaining to the plurality of entities, wherein the historical electrograms are collected prior to the one or more historical medical procedures and temporally correlated with the exemplary 108 Attorney Docket No.1518-164PCT1medical images; and the exemplary electro-anatomical mappings comprise historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures.

13. The apparatus of claim 12, wherein: the historical medical images comprise one or more of historical CT scans, historical MRI scans, and historical ultrasound data; the historical electrograms comprise historical ECGs; and the historical electro-anatomical mappings comprise historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures.

14. A method for generating electro-anatomical mapping, the method comprising: receiving, by a processor, input data, wherein receiving the input data comprises: receiving, from an imaging device, at least a medical image; and receiving, from a signal capturing device, at least an electrogram; generating, by the processor using at least a machine learning model, an electro- anatomical mapping as a function of the input data, wherein the at least a machine learning model is trained using electro-anatomical mapping training data comprising exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output; and displaying, by the processor using a user interface, the electro-anatomical mapping, wherein generating the electro-anatomical mapping comprises: generating a putative electro-anatomical mapping; validating the putative electro-anatomical mapping using a plurality of quality assurance parameters; and creating the electro-anatomical mapping by fine-tuning the putative electro- anatomical mapping as a function of an outcome of the validation.

15. The method of claim 14, wherein the at least a medical image comprises one or more of at least a computed tomography (CT) scan and at least a magnetic resonance imaging (MRI) scan.

16. The method of claim 14, wherein the at least an electrogram comprises at least an electrocardiogram (ECG). 109 Attorney Docket No.1518-164PCT117. The method of claim 14, wherein the input data further comprises one or more members from a group consisting of ultrasound data, point-of-care ultrasound (POCUS) data, intravascular ultrasound data, abdominal ultrasound data, at least a transthoracic echocardiogram (TTE), at least a transesophageal echocardiogram (TEE), at least an intracardiac echocardiogram (ICE), and at least a stress echocardiogram.

18. The method of claim 14, further comprising: identifying, by the processor, at least a target location pertaining to a medical procedure within the electro-anatomical mapping; and highlighting, by the processor, the at least a target location within the electro-anatomical mapping using the user interface.

19. The method of claim 18, wherein: the medical procedure comprises an ablation procedure; and the electro-anatomical mapping is used as an initial mapping for the ablation procedure.

20. The method of claim 19, further comprising: receiving, by the processor from a navigation system, a location of a catheter; and displaying, by the processor using the user interface, the location of the catheter on the electro-anatomical mapping.

21. The method of claim 20, further comprising modifying the electro-anatomical mapping as a function of the location of the catheter.

22. The method of claim 18, further comprising: identifying, by the processor, at least a medical feature within the electro-anatomical mapping; and adjusting, by the processor, at least a parameter pertaining to the medical procedure as a function of the at least a medical feature.

23. The method of claim 14, wherein generating the electro-anatomical mapping comprises aligning an electrical mapping with an anatomical mapping using fiducial point-based registration.

24. The method of claim 14, wherein the electro-anatomical mapping comprises a color- coded heat map.

25. The method of claim 14, wherein: the exemplary medical images comprise historical medical images pertaining to a 110 Attorney Docket No.1518-164PCT1plurality of entities and collected prior to one or more historical medical procedures; and the exemplary electro-anatomical mappings comprise historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures.

26. The method of claim 25, wherein: the historical medical images comprise one or more of historical CT scans, historical MRI scans, and historical ultrasound data; and the historical electro-anatomical mappings comprise historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures.

27. An apparatus for generating electro-anatomical mapping, the apparatus comprising: a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to: receive input data, wherein receiving the input data comprises: receiving, from an imaging device, at least a medical image; and receiving, from a signal capturing device, at least an electrogram; generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, wherein the machine learning model has been trained using electro-anatomical training data comprising historical medical images collected prior to one or more historical medical procedures and historical electrograms collected prior to the one or more historical medical procedures correlated to historical electro-anatomical mappings collected during the one or more historical medical procedures; and display the electro-anatomical mapping using a user interface.

28. The apparatus of claim 27, wherein generating the electro-anatomical mapping comprises: generating a putative electro-anatomical mapping; validating the putative electro-anatomical mapping using a plurality of quality assurance parameters; and 111 Attorney Docket No.1518-164PCT1creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation.

29. The apparatus of claim 27, wherein: exemplary medical images comprise historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures; exemplary electrograms comprise historical electrograms pertaining to the plurality of entities, wherein the historical signals are collected prior to the one or more historical medical procedures and temporally correlated with the exemplary medical images; and exemplary electro-anatomical mappings comprise historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures.

30. The apparatus of claim 29, wherein: the historical medical images comprise one or more of historical CT scans, historical MRI scans, and historical ultrasound data; the historical electrograms comprise historical ECGs; and the historical electro-anatomical mappings comprise historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures.

31. The apparatus of claim 27, wherein the at least an electrogram comprises at least an electrocardiogram (ECG).

32. The apparatus of claim 27, wherein the input data further comprises ultrasound data.

33. The apparatus of claim 27, wherein the processor is further configured modify the electro-anatomical mapping as a function of location data of a catheter, wherein modifying the electro-anatomical mapping comprises adjusting a color of a region in the electro-anatomical mapping corresponding to a catheter's position in response to changes in electrical potential.

34. The apparatus of claim 33, wherein the processor is further configured to: identify at least a target location pertaining to a medical procedure within the electro- anatomical mapping; and highlight the at least a target location within the electro-anatomical mapping using the user interface; 112 Attorney Docket No.1518-164PCT1updating a first view by replacing the first view with a second view as the location data of the catheter change; adjusting, using the location data of the catheter, a zoom level within the electro- anatomical map; correcting, using real-time data from the location data of the catheter, at least an error in the electro-anatomical map.

35. The apparatus of claim 34, wherein: the medical procedure comprises an ablation procedure, the ablation procedure comprising: receiving, from a navigation system, a location of a catheter; and displaying, using the user interface, the location of the catheter on the electro- anatomical mapping; and the electro-anatomical mapping is used as an initial mapping for the ablation procedure.

36. The apparatus of claim 27, wherein validating a putative electro-anatomical mapping using a plurality of quality assurance parameters comprises comparing, using the quality assurance parameters, the putative electro-anatomical mapping to one or more reference electro-anatomical mappings to determine a degree of correspondence between the putative electro-anatomical mapping and the reference electro-anatomical mappings.

37. A method for generating electro-anatomical mapping, wherein the method comprises: receiving, at a processor, input data, wherein receiving the input data comprises: receiving, from an imaging device, at least a medical image; and receiving, from a signal capturing device, at least an electrogram; generating, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, wherein the machine learning model has been trained using electro-anatomical training data comprising historical medical images collected prior to one or more historical medical procedures and historical electrograms collected prior to the one or more historical medical procedures correlated to historical electro-anatomical mappings collected during the one or more historical medical procedures; and displaying the electro-anatomical mapping using a user interface.

38. The method of claim 37, wherein generating the electro-anatomical mapping comprises: 113 Attorney Docket No.1518-164PCT1generating a putative electro-anatomical mapping; validating the putative electro-anatomical mapping using a plurality of quality assurance parameters; and creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation.

39. The method of claim 37, wherein: exemplary medical images comprise historical medical images pertaining to a plurality of entities and collected prior to one or more historical medical procedures; exemplary electrograms comprise historical electrograms pertaining to the plurality of entities, wherein the historical signals are collected prior to the one or more historical medical procedures and temporally correlated with the exemplary medical images; and exemplary electro-anatomical mappings comprise historical electro-anatomical mappings pertaining to the plurality of entities and collected during the one or more historical medical procedures.

40. The method of claim 39, wherein: the historical medical images comprise one or more of historical CT scans, historical MRI scans, and historical ultrasound data; the historical electrograms comprise historical ECGs; and the historical electro-anatomical mappings comprise historical cardiac electro-anatomical mappings pertaining to one or more ablation procedures.

41. The method of claim 37, wherein the at least an electrogram comprises at least an electrocardiogram (ECG).

42. The method of claim 37, wherein the input data further comprises ultrasound data.

43. The method of claim 37, wherein at least a processor is further configured modify the electro-anatomical mapping as a function of location data of a catheter, wherein modifying the electro-anatomical mapping comprises adjusting a color of a region in the electro-anatomical mapping corresponding to a catheter's position in response to changes in electrical potential.

44. The method of claim 43, further comprising: identifying, using the processor, at least a target location pertaining to a medical 114 Attorney Docket No.1518-164PCT1procedure within the electro-anatomical mapping; and highlighting, using the processor, the at least a target location within the electro- anatomical mapping using the user interface; updating a first view by replacing the first view with a second view as the location data of the catheter change; adjusting, using the location data of the catheter, a zoom level within the electro- anatomical map; and correcting, using real-time data from the location data of the catheter, at least an error in the electro-anatomical map.

45. The method of claim 44, wherein: the medical procedure comprises an ablation procedure, the ablation procedure comprising: receiving, from a navigation system, a location of a catheter; and displaying, using the user interface, the location of the catheter on the electro- anatomical mapping; and the electro-anatomical mapping is used as an initial mapping for the ablation procedure.

46. The method of claim 37, wherein validating a putative electro-anatomical mapping using a plurality of quality assurance parameters comprises comparing, using the quality assurance parameters, the putative electro-anatomical mapping to one or more reference electro-anatomical mappings to determine a degree of correspondence between the putative electro-anatomical mapping and the reference electro-anatomical mappings.

47. An apparatus for generating electro-anatomical mapping, wherein the apparatus comprises: a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to: receive input data, wherein the input data comprises at least a medical image and at least an electrogram; generate an electro-anatomical mapping as a function of the input data, wherein generating the electro-anatomical mapping comprises: generating a putative electro-anatomical mapping; 115 Attorney Docket No.1518-164PCT1validating the putative electro-anatomical mapping using a plurality of quality assurance parameters; and creating the electro-anatomical mapping by fine-tuning the putative electro-anatomical mapping as a function of an outcome of the validation; and display the electro-anatomical mapping using a user interface.

48. The apparatus of claim 47, wherein generating the electro-anatomical mapping further comprises using a machine learning module to implement at least a machine learning model, wherein the machine learning model has been trained on electro-anatomical mapping training data comprising exemplary medical images and exemplary electrograms correlated to exemplary electro-anatomical mappings.

49. The apparatus of claim 47, wherein: validating the putative electro-anatomical mapping produces an outcome of validation; and the outcome of validation comprises a binary output.

50. The apparatus of claim 47, wherein: validating the putative electro-anatomical mapping produces an outcome of validation; and the outcome of validation comprises at least a confidence score.

51. The apparatus of claim 47, wherein generating the electro-anatomical mapping further comprises: generating an electrical mapping using a mapping catheter and a mapping system; and generating an anatomical mapping using an in vivo imaging system.

52. The apparatus of claim 51, wherein generating the electro-anatomical mapping further comprises aligning the electrical mapping with the anatomical mapping, where aligning the electrical mapping with the anatomical mapping comprises: selecting at least a fiducial point in each of the electrical mapping and the anatomical mapping; applying at least a transformation to at least one of the electrical mapping and the anatomical mapping as a function of the at least a fiducial point; and adjusting the electrical mapping and the anatomical mapping to a common coordinate 116 Attorney Docket No.1518-164PCT1system as a function of the at least a transformation.

53. The apparatus of claim 47, wherein displaying the electro-anatomical mapping further comprises applying a color-coded heat map to the electro-anatomical mapping, wherein applying a color-coded heat map to the electro-anatomical mapping comprises: associating a first region of the electro-anatomical mapping with a first electrical activity; displaying the first region using a first color; associating a second region of the electro-anatomical mapping with a second electrical activity; and displaying the second region using a second color different from the first color.

54. The apparatus of claim 53, wherein the color-coded heat map comprises a continuous color spectrum across spatial regions, the first color and the second color being selected from the continuous color spectrum such that adjacent regions within an electro- anatomical mapping transition smoothly between color values representing different electrical activities.

55. The apparatus of claim 47, wherein displaying the electro-anatomical mapping further comprises highlighting at least a target location within the electro-anatomical mapping.

56. The apparatus of claim 47, wherein: the processor is further configured to receive, from a navigation system, a location of a catheter; and displaying the electro-anatomical mapping further comprises: displaying, on the user interface, a visual indicator of the catheter location overlaid on the electro-anatomical mapping; and dynamically modifying at least one aspect of the electro-anatomical mapping as a function of the catheter location.

57. A method for generating electro-anatomical mapping, wherein the method comprises: receiving, by a processor, input data, wherein the input data comprises at least a medical image and at least an electrogram: generating, using the processor, an electro-anatomical mapping as a function of the input data, wherein generating the electro-anatomical mapping comprises: generating a putative electro-anatomical mapping; validating the putative electro-anatomical mapping using a plurality of quality 117 Attorney Docket No.1518-164PCT1assurance parameters; and creating the electro-anatomical mapping by fine-tuning the putative electro- anatomical mapping as a function of an outcome of the validation; and displaying the electro-anatomical mapping using a user interface.

58. The method of claim 57, wherein generating the electro-anatomical mapping further comprises using a machine learning module to implement at least a machine learning model, wherein the machine learning model has been trained on electro-anatomical mapping training data comprising exemplary medical images and exemplary electrograms correlated to exemplary electro-anatomical mappings.

59. The method of claim 57, wherein: validating the putative electro-anatomical mapping produces an outcome of validation; and the outcome of validation comprises a binary output.

60. The method of claim 57, wherein: validating the putative electro-anatomical mapping produces an outcome of validation; and the outcome of validation comprises at least a confidence score.

61. The method of claim 57, wherein generating the electro-anatomical mapping further comprises: generating an electrical mapping using a mapping catheter and a mapping system; and generating an anatomical mapping using an in vivo imaging system.

62. The method of claim 61, wherein generating the electro-anatomical mapping further comprises aligning the electrical mapping with the anatomical mapping, where aligning the electrical mapping with the anatomical mapping comprises: selecting at least a fiducial point in each of the electrical mapping and the anatomical mapping; applying at least a transformation to at least one of the electrical mapping and the anatomical mapping as a function of the at least a fiducial point; and adjusting the electrical mapping and the anatomical mapping to a common coordinate system as a function of the at least a transformation.

63. The method of claim 57, wherein displaying the electro-anatomical mapping further 118 Attorney Docket No.1518-164PCT1comprises applying a color-coded heat map to the electro-anatomical mapping, wherein applying a color-coded heat map to the electro-anatomical mapping comprises: associating a first region of the electro-anatomical mapping with a first electrical activity; displaying the first region using a first color; associating a second region of the electro-anatomical mapping with a second electrical activity; and displaying the second region using a second color different from the first color.

64. The method of claim 63, wherein the color-coded heat map comprises a continuous color spectrum across spatial regions, the first color and the second color being selected from the continuous color spectrum such that adjacent regions within an electro-anatomical mapping transition smoothly between color values representing different electrical activities.

65. The method of claim 57, wherein displaying the electro-anatomical mapping further comprises highlighting at least a target location within the electro-anatomical mapping.

66. The method of claim 57, wherein: the method further comprises receiving, from a navigation system, a location of a catheter; and displaying the electro-anatomical mapping further comprises: displaying, on the user interface, a visual indicator of the catheter location overlaid on the electro-anatomical mapping; and dynamically modifying at least one aspect of the electro-anatomical mapping as a function of the catheter location. 119 Attorney Docket No.1518-164PCT1

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