US-20260123992-A1 - APPARATUS AND METHOD OF DETERMINING A CARDIAC IMPLANT SIZE

US20260123992A1US 20260123992 A1US20260123992 A1US 20260123992A1US-20260123992-A1

Abstract

Described herein is an apparatus and method for determining a cardiac implant size. In some embodiments, an apparatus may include an ultrasonic imaging device, at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to, using the ultrasonic imaging device, collect a plurality of ultrasonic images, using a 3D cardiac model generation machine learning model trained on a training dataset comprising example ultrasonic images correlated with example 3D cardiac models, generate a 3D cardiac model based on the plurality of ultrasonic images, generate at least a cardiac measurement based on the 3D cardiac model, determine a cardiac implant size based on the at least a cardiac measurement, and display to a user the cardiac implant size.

Inventors

Suthirth Vaidya
Abhijith Chunduru
Rakesh Barve

Assignees

ANUMANA, INC.

Dates

Publication Date: 20260507
Application Date: 20241106

Claims (20)

1 . An apparatus for determining a cardiac implant size, the apparatus comprising: an ultrasonic imaging device; at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: using the ultrasonic imaging device, collect a plurality of ultrasonic images; using a 3D cardiac model generation machine learning model trained on a training dataset comprising example ultrasonic images correlated with example 3D cardiac models, generate a 3D cardiac model based on the plurality of ultrasonic images; generate at least a cardiac measurement and a cardiac implant placement comprising at least a location of a cardiac implant and a location of a component of the cardiac implant based on the 3D cardiac model; determine the cardiac implant size and a cardiac implant candidate quality based on the at least a cardiac measurement; determine a thrombus status using a thrombus machine learning model based on the plurality of plurality of ultrasonic images; and using a display, display to a user the cardiac implant size, the cardiac implant candidate quality, the cardiac implant placement and the thrombus status.
2 . The apparatus of claim 1 , wherein the cardiac implant size comprises a left atrial appendage occlusion device size.
3 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the processor to determine one or both of a cardiac implant placement and a cardiac implant candidate as a function of the 3D cardiac model.
4 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the processor to: receive at least an ultrasound localization datum representing one or both of position and angle of the ultrasonic imaging device; and generate, using the 3D cardiac model generation machine learning model, the 3D cardiac model as a function of the at least an ultrasound localization datum.
5 . The apparatus of claim 1 , wherein the plurality of ultrasonic images comprises one or more of transthoracic echocardiogram (TTE) images and point of care ultrasound (POCUS) images.
6 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the processor to: calculate a level of uncertainty at a plurality of locations on the 3D cardiac model, wherein the plurality of locations comprises a high uncertainty region and the level of uncertainty comprises one or more of epistemic uncertainty, aleatoric uncertainty, model parameter uncertainty, boundary uncertainty, uncertainty in time series data, predictive uncertainty, systematic uncertainty, model output uncertainty; receive a subsequent plurality of ultrasonic images of cardiac anatomy corresponding to a high uncertainty region of the 3D cardiac model, wherein the subsequent plurality of ultrasonic images is captured using the ultrasonic imaging device, as a function of the high uncertainty region; and generate a subsequent 3D cardiac model as a function of the subsequent plurality of ultrasonic images.
7 . The apparatus of claim 1 , wherein generating the 3D cardiac model based on the plurality of ultrasonic images comprises: generating a set of shape parameters representing a cardiac shape as a function of the plurality of ultrasonic images and a shape identification model trained on the training dataset; wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of a subject's heart and a plurality of associated parameter ranges, wherein at least a parameter range of the plurality of associated parameter ranges is based on a subset of possible values of a parameter that historical healthy structures commonly fall into, as determined from a dataset.
8 . The apparatus of claim 1 , wherein the memory contains instructions further configuring the processor to: receive a 3D cardiac implant model representing a cardiac implant; and display, using the display, the 3D cardiac implant model positioned within the 3D cardiac model.
9 . The apparatus of claim 1 , wherein the cardiac measurement comprises an ostial diameter of a left atrial appendage (LAA).
10 . The apparatus of claim 1 , wherein generating the 3D cardiac model based on the plurality of ultrasonic images comprises: generating a 3D voxel occupancy representation (VOR) representing a cardiac shape as a function of the plurality of ultrasonic images and the 3D cardiac model generation machine learning model trained on the training dataset; generating a mesh representing the cardiac shape as a function of the 3d voxel occupancy representation; and displaying, using the display, the mesh to the user.
11 . A method of determining a cardiac implant size, the method comprising: using at least a processor and an ultrasonic imaging device, collecting a plurality of ultrasonic images; using the at least a processor and a 3D cardiac model generation machine learning model trained on a training dataset comprising example ultrasonic images correlated with example 3D cardiac models, generating a 3D cardiac model based on the plurality of ultrasonic images; using the at least a processor, generating at least a cardiac measurement based on the 3D cardiac model and a cardiac implant placement comprising at least a location of a cardiac implant and a location of a component of the cardiac implant; using the at least a processor, determining the cardiac implant size and a cardiac implant candidate quality based on the at least a cardiac measurement; using the at least a processor, determining a thrombus status using a thrombus machine learning model based on the plurality of plurality of ultrasonic images; and using the at least a processor and a display, displaying to a user the cardiac implant, the cardiac implant candidate quality, the cardiac implant placement and the thrombus status.
12 . The method of claim 11 , wherein the cardiac implant size comprises a left atrial appendage occlusion device size.
13 . The method of claim 11 , wherein the method further comprises determining one or both of a cardiac implant placement and a cardiac implant candidate quality as a function of the 3D cardiac model.
14 . The method of claim 11 , wherein the method further comprises: receiving, using the at least a processor, at least an ultrasound localization datum representing one or both of position and angle of the ultrasonic imaging device; and generating, using the at least a processor and the 3D cardiac model generation machine learning model, the 3D cardiac model as a function of the at least an ultrasound localization datum.
15 . The method of claim 11 , wherein the plurality of ultrasonic images comprises one or more of transthoracic echocardiogram (TTE) images, and point of care ultrasound (POCUS) images.
16 . The method of claim 11 , wherein the method further comprises: calculating, using the at least a processor, a level of uncertainty at a plurality of locations on the 3D cardiac model, wherein the plurality of locations comprises a high uncertainty region, and the level of uncertainty comprises one or more of epistemic uncertainty, aleatoric uncertainty, model parameter uncertainty, boundary uncertainty, uncertainty in time series data, predictive uncertainty, systematic uncertainty, model output uncertainty; receiving, using the at least a processor, a subsequent plurality of ultrasonic images of cardiac anatomy corresponding to a high uncertainty region of the 3D cardiac model, wherein the subsequent plurality of ultrasonic images is captured using the ultrasonic imaging device, as a function of the high uncertainty region; and generating, using the at least a processor, a subsequent 3D cardiac model as a function of the subsequent plurality of ultrasonic images.
17 . The method of claim 11 , wherein generating the 3D cardiac model based on the plurality of ultrasonic images comprises: generating a set of shape parameters representing a cardiac shape as a function of the plurality of ultrasonic images and a shape identification model trained on the training dataset; wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of a subject's heart and a plurality of associated parameter ranges, wherein at least a parameter range of the plurality of associated parameter ranges is based on a subset of possible values of a parameter that historical healthy structures commonly fall into, as determined from a dataset.
18 . The method of claim 11 , wherein the method further comprises: receiving, using the at least a processor, a 3D cardiac implant model representing a cardiac implant; and displaying, using the at least a processor and the display, the 3D cardiac implant model positioned within the 3D cardiac model.
19 . The method of claim 11 , wherein the cardiac measurement comprises an ostial diameter of a left atrial appendage (LAA).
20 . The method of claim 11 , wherein the generating the 3D cardiac model based on the plurality of ultrasonic images comprises: generating, using the at least a processor, a 3D voxel occupancy representation (VOR) representing a cardiac shape as a function of the plurality of ultrasonic images and the 3D cardiac model generation machine learning model trained on the training dataset; generating, using the at least a processor, a mesh representing the cardiac shape as a function of the 3D voxel occupancy representation; and displaying, using the at least a processor and the display, the mesh to the user.

Description

FIELD OF THE INVENTION The present invention generally relates to the field of cardiac imaging. In particular, the present invention is directed to an apparatus and method of determining a cardiac implant size. BACKGROUND Computed tomography (CT) has been shown to be superior to 2D TEE in Watchman pre-procedural device sizing. As per the PRO3DLAAO clinical trial, the accuracy for 1st occluder device selection is 92% with CT vs. 27% with 2D-TEE, using the final implanted Watchman device size as the reference standard. The poor accuracy of the initial device selection results in longer procedures and usage of multiple devices until the implantation can be verified to be leakage-free. That is, an average of 2.5 devices is used with 2D TEE-based planning vs. 1.3 devices used with CT-based planning. However, computed tomography exposes subjects to radiation, and is often performed on a separate date, requiring multiple appointments and potentially reducing accuracy due to the time delay between when the data is captured and when it is used. SUMMARY OF THE DISCLOSURE In an aspect, described herein is an apparatus for determining a cardiac implant size. Such an apparatus may include an ultrasonic imaging device, at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to, using the ultrasonic imaging device, collect a plurality of ultrasonic images, using a 3D cardiac model generation machine learning model trained on a training dataset comprising example ultrasonic images correlated with example 3D cardiac models, generate a 3D cardiac model based on the plurality of ultrasonic images, generate at least a cardiac measurement based on the 3D cardiac model, determine a cardiac implant size based on the at least a cardiac measurement, and display to a user the cardiac implant size. In another aspect, described herein is a method of determining a cardiac implant size, the method comprising, using at least a processor and an ultrasonic imaging device, collecting a plurality of ultrasonic images, using the at least a processor and a 3D cardiac model generation machine learning model trained on a training dataset comprising example ultrasonic images correlated with example 3D cardiac models, generating a 3D cardiac model based on the plurality of ultrasonic images, using the at least a processor, generating at least a cardiac measurement based on the 3D cardiac model, using the at least a processor, determining a cardiac implant size based on the at least a cardiac measurement, and using the at least a processor, displaying to a user the cardiac implant size. These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG. 1 is a diagram depicting an exemplary embodiment of an apparatus for determining a cardiac implant size; FIG. 2 shows an exemplary embodiment of an intracardiac echocardiography (ICE) image; FIG. 3 is a flow diagram of an exemplary embodiment of an ICE image example generation process; FIG. 4 illustrates an exemplary embodiment of a three-dimensional (3D) voxel occupancy representation; FIG. 5 is a schematic diagram of an exemplary transesophageal echocardiogram; FIG. 6 presents 2D transesophageal echocardiogram (TEE) views at varying orientations; FIG. 7 is a block diagram of an exemplary embodiment of a machine learning model; FIG. 8 is a schematic diagram of an exemplary embodiment of a neural network; FIG. 9 is a schematic diagram of an exemplary embodiment of a neural network node; FIG. 10 is a flow diagram depicting an exemplary embodiment of a method of determining a cardiac implant size; FIG. 11 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. DETAILED DESCRIPTION At a high level, an apparatus and method for determining a cardiac implant size is disclosed. An apparatus may include an ultrasonic imaging device and a computing device. Such a computing device may receive ultrasonic images of a subject's heart, such as transesophageal echocardiograms (TEE) and/