26 results about "Anatomical measurement" patented technology

Systems and methods for fully automated anatomical analysis of an anatomical structure are provided to facilitate pre-operative planning. The computerized method may include obtaining a plurality of images, e.g., MSCT images, of patient-specific cardiovascular anatomy, and analyzing the MSCT images with a trained artificial intelligence module to identify one or more anatomical landmarks and to construct a virtual three-dimensional model of the anatomical structure. For example, the trained artificial intelligence module may execute segmentation, point detection, curve detection, or plane detection deep learning modules, independently or in combination, to identify the anatomical landmarks. The method further may include deriving anatomical measurements of the one or more identified anatomical landmarks, and displaying the virtual three-dimensional model alongside the anatomical measurements of the one or more identified anatomical landmarks.

A method for optimizing a knee arthroplasty surgical procedure includes receiving pre-operative data comprising (i) anatomical measurements of the patient, (ii) soft tissue measurements of the patient's anatomy, and (iii) implant parameters identifying an implant to be used in the knee arthroplasty surgical procedure. An equation set is selected from a plurality of pre-generated equation sets based on the pre-operative data. During the knee arthroplasty surgical procedure, patient-specific kinetic and kinematic response values are generated and displayed using an optimization process. The optimization process includes collecting intraoperative data from one or more surgical tools of a computer-assisted surgical system, and using the intraoperative data and the pre-operative data to solve the equation set, thereby yielding the patient-specific kinetic and kinematic response values. A visualization is then provided of the patient-specific kinetic and kinematic response values on the displays.

A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.

This invention provides a system and method for determining the specifications of a left atrial appendage closure device based on two-dimensional (2D) intracardiac echocardiography (ICE). [Solution] In imaging procedures, 2D-ICE is used to acquire images of the patient's left atrium. A model of the left atrium is generated from the images of the left atrium. The model of the left atrium is used to validate subsequent imaging of the patient's left atrial appendage. Anatomical measurements from the validated subsequent imaging are used to select or identify a left atrial appendage closure device, for example, for the treatment of atrial fibrillation patients.

A system and method configured to better identify patient-specific anatomical landmarks, measure anatomical parameters and features, and predict the patient's need for surgery within a predetermined time period. In some embodiments, the system and method is configured to predict the likelihood or risk that a patient will require total hip arthroplasty. In some embodiments, the present invention includes machine learning technology Some embodiments of the present invention include a first ML machine configured to received medical images as inputs and identify anatomical landmarks as outputs; a measurement module to measure joint space width, hip dysplasia angles, and / or leg length differential; and a second ML machine configured to receive the anatomical measurements and patient demographic data as inputs and produce a risk or likelihood that the patient will require surgery within a certain time frame.

Surgical planning based on a three-dimensional (3D) model includes obtaining a 3D model of an anatomical region, wherein the anatomical region includes the patient's anatomical features; identifying at least one deformity, wherein the at least one deformity is identified with respect to a target anatomical value of the patient's anatomical features, wherein the target anatomical value includes a desired range into which the anatomical measurement should fall; and using the anatomical measurement, determining at least one planar correction to be made to at least one anatomical structure of the patient. Generating a 3D model using two-dimensional (2D) imaging data may include performing anatomical context processing, which includes annotating the model, contours, edges, or surfaces of the anatomical structure, as well as performing 2D-to-3D reconstruction to optimize the orientation, placement, and scale of the 3D digital volume modeling the anatomical structure, resulting in a 3D anatomical representation of the anatomical region as a 3D model.

Left atrial appendage occlusion device specifications from 2D ICE for use in the treatment of atrial fibrillation patients. Systems and methods for left atrial appendage occlusion device specification from two-dimensional (2D) intracardiac echo imaging (ICE). In the imaging procedure, the 2D ICE is used to acquire an image of the patient's left atrium. A model of the left atrium is generated from the image of the left atrium. The model of the left atrium is used to validate subsequent imaging of the left atrial appendage of the patient. Anatomical measurements from validated subsequent imaging may be used to select or specify a left atrial appendage occlusion device in order, for example, to treat atrial fibrillation patients.

Systems, methods, and apparatuses for preparing a device, such as a wheelchair, for assembly are provided. Anatomical measurements of an intended user of the device may be received. Frame properties for the device may be determined. The frame properties may be determined based on the one or more anatomical measurements of the user. A material for constructing part or all of the frame of the device may be determined. The material may be determined based on the frame properties. One or more parts for the frame may be converted from a three-dimensional shape to a two-dimensional part shape. A nesting arrangement for the parts of the device to be cut from the material may be determined on a sheet of the material. A bending order for bending the parts may also be determined. The parts of the device may be cut from the sheet material, bent, and assembled.

A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.

The utility model relates to the technical field of zooplankton experiment instruments. A novel counting frame integrating zooplankton classification counting and individual anatomy measurement comprises a counting frame body, and the counting frame body comprises an anatomy area and a counting area which are arranged front and back. A strip-shaped notch is formed in the dissecting area and is connected with a dissecting bearing sheet in a sliding manner; a scale mark is arranged on the counting frame main body, and the scale mark is adjacent to the strip-shaped notch; the counting area is provided with a circuitous groove, the groove comprises transverse channels which are arranged from front to back, the adjacent transverse channels are communicated with each other through a connecting channel, the end part, far away from the dissecting area, of the groove extends to the edge of the counting frame main body, and the end part, far away from the dissecting area, of the groove is detachably connected with a plugging piece. According to the zooplankton dissecting device, zooplankton needing to be dissected can be conveniently and directly moved to the dissecting bearing piece to be And the length of the individual can be measured through the scale marks.

A system may receive an MRI image. A system may receive a user input comprising a requested anatomical measurement. A system may input, into a trained supervised machine learning model, the MRI image and the requested anatomical measurement. A system may segment, using the trained supervised machine learning model, a plurality of endpoint regions associated with the requested anatomical measurement from the MRI image. A system may determine a two-dimensional (2D) measurement based on the plurality of endpoint regions.

Systems and methods for evaluating a proposed valve-in-valve procedure for a patient in which a replacement transcatheter aortic valve will be deployed within a first bioprosthetic aortic valve. The methods include selecting predetermined benchmark measurements of a valve-in-valve combination. Images of anatomy of the patient are received. Anatomical measurements of the first bioprosthetic valve are obtained from the received images. The predetermined benchmark measurements and the anatomical measurements are reviewed. Based, at least in part, upon the review, risks of a valve-in-valve procedure for the patient are evaluated. The methods of the present disclosure can be used on baseline scans of a patient without a first bioprosthetic valve implanted; under these circumstances, dimensions of the first valve are determined by benchmark measurements. Where methods of the present disclosure are used on post-first implant scans, then the dimensions of the first valve are determined from the post-implant scans.

A sports helmet kit has a shell, attachable face guard, composite helmet liner, and fit pods to improve and customize the fit of the helmet to the wearer. The composite liner consists of a base liner and a selected group of fit elements, for example, fit pods, removably attached to the inner surface of the base liner (i.e., the surface of the base liner facing the wearer's head). The fit pods are selected from a set of fit pods having different properties, for example, different sizes, thicknesses, densities, and cross-sections. The selection of fit pods from the set may be aided by taking anatomical measurements of the wearer's head and analyzing the measurements with respect to the geometry of the helmet to produce a pressure map. The measurements may be taken by physical contact or by non-contact means. The fit pods may be selected to optimize a pressure map, and thus optimize the fit, for a given wearer of the helmet.

An anatomy visualization device may include a display, an optical sensor, and processing circuitry. The processing circuitry may be configured to capture, via the optical sensor, body registration points on a body of an individual, receive external anatomical measurements of the body, apply the external anatomical measurements to a body shape model, and determine an organ placement prediction for the body based on the application of external anatomical measurements to the body shape model. The organ placement prediction may include organ position information, organ size information, and organ shape information for a plurality of organs. The processing circuitry may also align the organ placement prediction with the body registration points and render, on the display, the organ placement prediction in alignment with the body registration points as an augmented reality object on the body.

The utility model discloses a measuring tool for determining an anatomical measuring point, relates to the technical field of measuring tools, and aims to solve the problems that a sliding block is easy to slide, the position is unstable and measuring errors are easy to generate in the anatomical measuring process in the prior art. A strip-shaped sliding groove is formed in the front end of the interior of the measuring plate, main alignment points are symmetrically arranged in the measuring plate along the two sides of the strip-shaped sliding groove respectively, main scale marks are symmetrically arranged on the front end face of the measuring plate along the upper portion and the lower portion of the strip-shaped sliding groove respectively, and a sliding plate is arranged at the front end of the strip-shaped sliding groove of the measuring plate. A sliding block is arranged in the strip-shaped sliding groove of the measuring plate, the sliding block is fixedly connected with the sliding plate, a stabilizing mechanism is arranged in the sliding block, and the stabilizing mechanism comprises a straight rod, a springing plate, a compression spring, a mounting plate and a pressing rod.

A sports helmet kit has a shell, attachable face guard, composite helmet liner, and fit pods to improve and customize the fit of the helmet to the wearer. The composite liner consists of a base liner and a selected group of fit elements, for example, fit pods, removably attached to the inner surface of the base liner (i.e., the surface of the base liner facing the wearer's head). The fit pods are selected from a set of fit pods having different properties, for example, different sizes, thicknesses, densities, and cross-sections. The selection of fit pods from the set may be aided by taking anatomical measurements of the wearer's head and analyzing the measurements with respect to the geometry of the helmet to produce a pressure map. The measurements may be taken by physical contact or by non-contact means. The fit pods may be selected to optimize a pressure map, and thus optimize the fit, for a given wearer of the helmet.

Disclosed herein is a medical system (100, 300, 500). The execution of machine executable instructions (112) causes a computational system (104) to: receive (200) a baseline anatomical measurement (114) descriptive of a clinical magnetic resonance image of a subject (318); receive (202) scan metadata (116) descriptive of the clinical magnetic resonance image of the subject; send (204) scan parameters via a network connection (350) to a low-field magnetic resonance imaging system (301); receive (206) subsequent k-space data (122) from the low-field magnetic resonance imaging system via the network connection in response to sending the scan parameters; reconstruct (208) a subsequent magnetic resonance image (124) from the subsequent k-space data; determine (210) a subsequent anatomical measurement (128) in response to inputting the subsequent magnetic resonance image into the segmentation module; and provide (212) a warning signal (132) if the subsequent anatomical measurement varies from the baseline anatomical measurement by more than a predetermined amount.

This application discloses a multimodal electromagnetic navigation-assisted transnasal endoscopic anatomical measurement device and method for a fresh cadaver head. The method is applied to the control module of the device, which also includes an endoscope module and an electromagnetic tracking module. The method includes: obtaining multimodal image information corresponding to the fresh cadaver head to be measured; generating a three-dimensional model of the fresh cadaver head based on the multimodal image information; obtaining a target position corresponding to a preset key anatomical structure in the three-dimensional model; the key anatomical structure includes at least one or more of the nasal cavity, sinuses, blood vessels, and nerves; when the endoscope module enters the nasal cavity of the fresh cadaver head, obtaining magnetic field information sent by the electromagnetic tracking module; calculating the spatial position of the electromagnetic tracking module in the three-dimensional model based on the magnetic field information; generating navigation information corresponding to the endoscope module based on the spatial position and the target position, the navigation information including direction information and distance information; and moving the endoscope module to the target position based on the navigation information.

For anatomy measurement in a surgical system, a machine-learned model is used to determine the location or length. For example, in RYGB, the machine-learned model indicates how much of the bowel has been pulled or run. As another RYGB example, the machine-learned model indicates a length along the bowel to a current location. In yet another RYGB example, the machine-learned model indicates a location for incision based on a desired length. Using an image or images, such as a video stream from an endoscope, the artificial intelligence provides the measurement, avoiding running with estimated steps size or use an inserted ruler.

Anatomical measurements of non-visible aspects of features of living organisms that include a visible contoured shape on the anatomical region of the living organism is disclosed. The system and method include an imager configured to take a series of images of an anatomical region of a living organism, such as a human patient in a clinic, doctor's office, or hospital. The system and method create a three-dimensional digital anatomical model of the exterior or visible part of the anatomical region that includes a target feature, such as a breast, nose, foot, or tumor. The target feature within the three-dimensional digital model is then isolated and manipulated to find measurements of non-visible aspects of the feature, such as mass, distances between visible points which pass through invisible tissues, e.g., depth and base width, surface distance between visible points, volume, area, surface area, circumference, and surface angle measurements of the feature.

Systems and methods for fully automated anatomical analysis of an anatomical structure are provided to facilitate pre-operative planning. The computerized method may include obtaining a plurality of images, e.g., MSCT images, of patient-specific cardiovascular anatomy, and analyzing the MSCT images with a trained artificial intelligence module to identify one or more anatomical landmarks and to construct a virtual three-dimensional model of the anatomical structure. For example, the trained artificial intelligence module may execute segmentation, point detection, curve detection, or plane detection deep learning modules, independently or in combination, to identify the anatomical landmarks. The method further may include deriving anatomical measurements of the one or more identified anatomical landmarks, and displaying the virtual three-dimensional model alongside the anatomical measurements of the one or more identified anatomical landmarks.

Image-guided navigation for spinal cord treatments and therapies are described. The image-guided navigation is augmented with anatomical measurement data related to spinal cord and vertebral anatomy. From these data and medical image data, an augmented model of spinal cord anatomy is generated and / or navigation data can be generated for localizing spinal cord structures, such as by mapping the anatomical measurement data to the medical image data. The augmented model data and / or navigation data can be used for surgical navigation, stimulation parameter setting, electrode configuration selection, pre-surgical planning, surgical visualization, and so on.

The present disclosure describes an imaging system configured to determine the accuracy of anatomical measurements obtained from image data. The system can include an ultrasound transducer configured to acquire echo signals in response to ultrasound pulses transmitted toward a target region. The system can also include a graphical user interface configured to display a biometric tool widget, such as a caliper, for acquiring measurements of anatomical features within the target region from at least one image frame generated from the ultrasound echoes. The system can also include one or more processors configured to determine a confidence metric indicating the accuracy of the measurement. The processor can also be configured to cause the graphical user interface to display a graphical indicator corresponding to the confidence metric. The processor can implement one or more neural networks and can derive additional information, such as gestational age or weight, from the acquired anatomical measurements.

The pillow is created to allow sleeping in three positions—left side, back side, and right side. The shapes and sizes of the pillow are taken from anatomical measurements of the body to support the sleeper's size, position and comfort needs. The side position is the height of the sleepers shoulder. The side positions top also tilts the face slightly upward and has a large divot for the ear. The center of the pillow is made for sleeping on the back. It raises the shoulders slightly, and supports the neck. Behind the neck support is a concave depression that will cradle and support the head. However, the pillow does not support the head until it is tilted backwards slightly. The backward tilt of the head should reduce snoring and sleep apnea. The pillow may have 3 layers—base layer, cushion layer, and contact layer.

Systems and methods for left atrial appendage closure device specification from two dimensional (2D) Intracardiac Echo imaging (ICE). In an imaging procedure, 2D ICE is used to acquire images of the left atrium of a patient. A model of the left atrium is generated from the images of the left atrium. The model of the left atrium is used to validate subsequent imaging of the left atrial appendage of the patient. Anatomical measurements from the validated subsequent imaging may be used to select or specify a left atrial appendage closure device in order to, for example, treat atrial fibrillation patients.

Surgical planning based on three-dimensional (3D) models includes obtaining two-dimensional (2D) imaging data of an anatomical region, of a patient, having patient anatomical features, generating a three-dimensional (3D) model of the anatomical region, the 3D model being specific to the patient and providing a 3D representation of the patient anatomical features, identifying at least one deformity exhibited in the 3D model and identified based on the 3D model and relative to target anatomical values for the patient anatomical features, where the identifying includes obtaining anatomical measurements based on anatomical landmarks of the patient as exhibited in the 3D model, comparing the anatomical measurements to the target anatomical values, and determining the at least one deformity based on the comparing, and determining, based on a relationship between the anatomical measurements and the target anatomical values, at least one correction to make to at least one anatomical structure of the patient.