Apparatus and method of determining a cardiac implant size

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.