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

• URL: http://arxiv.org/abs/2308.00009v2
• Date: Tue, 26 Nov 2024 19:40:27 GMT
• Title: A 3D deep learning classifier and its explainability when assessing coronary artery disease
• Authors: Wing Keung Cheung, Jeremy Kalindjian, Robert Bell, Arjun Nair, Leon J. Menezes, Riyaz Patel, Simon Wan, Kacy Chou, Jiahang Chen, Ryo Torii, Rhodri H. Davies, James C. Moon, Daniel C. Alexander, Joseph Jacob,
• Abstract summary: Early detection and diagnosis of coronary artery disease could save lives and reduce healthcare costs. Most current approaches utilise deep learning methods but require centerline extraction and multi-planar reconstruction. Our proposed approach outperforms the state-of-the-art models by 21.43% in terms of classification accuracy.
• Score: 2.749052158388996
• License:
• 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.

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