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

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.

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