Explainable deep learning camera-agnostic diagnosis of obstructive coronary artery disease

Patent metadata

Field	Value
Publication number	US-2023309940-A1
Application number	US-202118023092-A
Country	US
Kind code	A1
Filing date	Aug 27, 2021
Priority date	Aug 28, 2020
Publication date	Oct 5, 2023
Grant date	—

Abstract

A deep learning model for the detection of obstructive coronary artery disease (CAD) can take a set of polar maps and patient information as input, then output obstructive CAD scoring data, such as probabilities of obstructive CAD associated with various cardiac territories, as well as an attention map and a CAD scoring map. The model can operate agnostic of camera type used to capture the set of polar maps. The attention map indicates regions of the polar maps important to the deep learning process for that particular set of polar maps. The attention map and obstructive CAD scoring data can be used to generate a CAD scoring map showing CAD probability by segment on a standard 17-segment model of a left ventricle. The attention map and/or CAD scoring map can act as easily explainable tools for interpreting the results of a myocardial perfusion imaging study.

First claim

What is claimed is:

1 . A system, comprising:
one or more data processors; and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including:
receiving input data associated with a myocardial perfusion imaging study of a patient, wherein the input data comprises a set of polar maps and patient information, wherein the set of polar maps comprises a perfusion polar map, a motion polar map, and a thickening polar map, and wherein the patient information comprises at least one selected from the set consisting of sex information, age information, and cardiac volume information;
providing the input data to a deep learning model, wherein the deep learning model comprises an input layer, at least one convolution layer, at least one pooling layer, and at least one fully connected layer;
generating obstructive coronary artery disease (CAD) scoring data in response to providing the input data to the deep learning model, wherein the obstructive CAD scoring data is indicative of i) a general probability for obstructive CAD, ii) a probability for obstructive CAD in a left anterior descending artery (LAD) territory, iii) a probability for obstructive CAD in a left circumflex artery (LCx) territory, iv) and a probability for obstructive CAD in a right coronary artery (RCA) territory, or v) any combination of i-iv;
generating an attention map in response to providing the input data to the deep learning model, wherein the attention map is indicative of regions of importance associated with the obstructive CAD scoring data; and
presenting, on a display device, the obstructive CAD scoring data and the attention map.

2 . The system of claim 1 , wherein generating the attention map comprises applying gradients of a predicted vessel to a final convolution layer of the at least one convolution layer to produce the attention map, wherein the regions of importance are indicative of regions of one or more polar maps of the set of polar maps with high informational weight in the deep learning model.

3 . The system of claim 1 , further comprising:
generating a coronary artery disease probability map based on the attention map, the coronary artery disease probability map comprising a set of segments indicative of segments of a left ventricle, wherein generating the coronary artery disease probability map comprises determining severity measurements for each segment of the set of segments using the attention map and the obstructive CAD scoring data; and presenting, on the display device, the coronary artery disease probability map.

4 . The system of claim 1 , wherein the deep learning model was previously trained using a first set of training data collected using a vacuum-tube-photomultiplier-based camera and a second set of training data collected using a cadmium-zinc-telluride-based camera.

5 . The system of claim 1 , wherein providing the input data to the deep learning model comprises:
providing the set of polar maps to the input layer of the deep learning model; and providing the patient information to the at least one fully connected layer of the deep learning model.

6 . The system of claim 5 , wherein the at least one fully connected layer includes a fully connected output layer, and wherein providing the patient information to the at least one fully connected layer comprises providing the patient information to the fully connected output layer.

7 . The system of claim 1 , wherein providing the set of polar maps to the deep learning model comprises providing the set of polar maps without pre-defined coronary territories.

8 . A computer-implemented method, comprising:
receiving input data associated with a myocardial perfusion imaging study of a patient, wherein the input data comprises a set of polar maps and patient information, wherein the set of polar maps comprises a perfusion polar map, a motion polar map, and a thickening polar map, and wherein the patient information comprises at least one selected from the set consisting of sex information, age information, and cardiac volume information; providing the input data to a deep learning model, wherein the deep learning model comprises an input layer, at least one convolution layer, at least one pooling layer, and at least one fully connected layer; generating obstructive coronary artery disease (CAD) scoring data in response to providing the input data to the deep learning model, wherein the obstructive CAD scoring data is indicative of i) a general probability for obstructive CAD, ii) a probability for obstructive CAD in a left anterior descending artery (LAD) territory, iii) a probability for obstructive CAD in a left circumflex artery (LCx) territory, iv) a probability for obstructive CAD in a right coronary artery (RCA) territory, or v) any combination of i-iv; generating an attention map in response to providing the input data to the deep learning model, wherein the attention map is indicative of regions of importance associated with the obstructive CAD scoring data; presenting, on a display device, the obstructive CAD scoring data and the attention map.

9 . The method of claim 8 , wherein generating the attention map comprises applying gradients of a predicted vessel to a final convolution layer of the at least one convolution layer to produce the attention map, wherein the regions of importance are indicative of regions of one or more polar maps of the set of polar maps with high informational weight in the deep learning model.

10 . The method of claim 8 , further comprising generating a coronary artery disease probability map based on the attention map, the coronary artery disease probability map comprising a set of segments indicative of segments of a left ventricle, wherein generating the coronary artery disease probability map comprises determining severity measurements for each segment of the set of segments using the attention map and the obstructive CAD scoring data.

11 . The method of claim 8 , wherein the deep learning model was previously trained using a first set of training data collected using a vacuum-tube-photomultiplier-based camera and a second set of training data collected using a cadmium-zinc-telluride-based camera.

12 . The method of claim 8 , wherein providing the input data to the deep learning model comprises:
providing the set of polar maps to the input layer of the deep learning model; and providing the patient information to the at least one fully connected layer of the deep learning model.

13 . The method of claim 12 , wherein the at least one fully connected layer includes a fully connected output layer, and wherein providing the patient information to the at least one fully connected layer comprises providing the patient information to the fully connected output layer.

14 . The method of claim 8 , wherein providing the set of polar maps to the deep learning model comprises providing the set of polar maps without pre-defined coronary territories.

15 . A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to perform operations including:
receiving input data associated with a myocardial perfusion imaging study of a patient, wherein the input data comprises a set of polar maps and patient information, wherein the set of polar maps comprises a perfusion polar map, a motion polar map, and a thickening polar map, and wherein the patient information comprises at least o

Assignees

• Cedars Sinai Medical Center

Inventors

• Slomka Piotr
• Singh Ananya
• Kavanagh Paul
• Cadet Sebastien

Classifications

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H30/40

  for processing medical images, e.g. editing · CPC title

• A61B6/503 Primary

  for diagnosis of the heart · CPC title

• A61B6/5217

  extracting a diagnostic or physiological parameter from medical diagnostic data · CPC title