A 3D deep learning classifier and its explainability when assessing coronary artery disease

TL;DR

This paper introduces a 3D deep learning model for direct CAD classification from CTCA images and a 2D U-Net for artery segmentation, offering improved accuracy and explainability for clinical use.

Contribution

It presents a novel combined 3D classification and 2D segmentation approach that enhances accuracy and explainability in CAD diagnosis from CTCA images.

Findings

Outperforms state-of-the-art models by 21.43% in accuracy.

Provides better heat maps with focal loss.

Offers more explainability than existing methods.

Abstract

Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs. The current clinical practice is to perform CAD diagnosis through analysing medical images from computed tomography coronary angiography (CTCA). Most current approaches utilise deep learning methods but require centerline extraction and multi-planar reconstruction. These indirect methods are not designed in a clinician-friendly manner, and they complicate the interventional procedure. Furthermore, the current deep learning methods do not provide exact explainability and limit the usefulness of these methods to be deployed in clinical settings. In this study, we first propose a 3D Resnet-50 deep learning model to directly classify normal subjects and CAD patients on CTCA images, then we demonstrate a 2D modified U-Net model can be subsequently employed to segment the coronary arteries. Our proposed approach outperforms the state-of-the-art models by 21.43% in terms of classification accuracy. The classification model with focal loss provides a better and more focused heat map, and the segmentation model provides better explainability than the classification-only model. The proposed holistic approach not only provides a simpler and clinician-friendly solution but also good classification accuracy and exact explainability for CAD diagnosis.