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

A computer-implemented method for detecting coronary obstructions, comprising:

• creating a first set of training data associated with a myocardial perfusion imaging study comprising data collected using a first type of camera and a second set of training data associated with a myocardial perfusion imaging study collected using a second type of camera wherein the second type of camera is a different type of camera than the first type of camera;
• training a deep learning model using the first set of training data and the second set of training data, 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;
• receiving input data associated with a myocardial perfusion imaging study of a patient, wherein the input data comprises a set of polar maps generated by a camera 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 excluding the type of camera used to generate the set of polar maps to the deep learning model
• 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.

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:

• creating a first set of training data associated with a myocardial perfusion imaging study comprising data collected using a first type of camera and a second set of training data associated with a myocardial perfusion imaging study collected using a second type of camera wherein the second type of camera is a different type of camera than the first type of camera;
• training a deep learning model using the first set of training data and the second set of training data, 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;
• receiving input data associated with a myocardial perfusion imaging study of a patient, wherein the input data comprises a set of polar maps generated by a camera 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 excluding the type of camera used to generate the set of polar maps to the deep learning model
• 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.

A method for treating obstructive coronary artery disease (CAD), comprising:

• creating a first set of training data associated with a myocardial perfusion imaging study comprising data collected using a first type of camera and a second set of training data associated with a myocardial perfusion imaging study collected using a second type of camera wherein the second type of camera is a different type of camera than the first type of camera;
• training a deep learning model using the first set of training data and the second set of training data, 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;
• receiving input data associated with a myocardial perfusion imaging study of a patient, wherein the input data comprises a set of polar maps generated by a camera 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 excluding the type of camera used to generate the set of polar maps to the deep learning model
• 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;
• presenting, on a display device, the obstructive CAD scoring data and the attention map;
• applying a treatment for obstructive CAD based on the CAD scoring data.