Automatic CAD-RADS Scoring Using Deep Learning

TL;DR

This paper introduces a deep learning method for fully automated CAD-RADS scoring from coronary CT angiography, enabling efficient and accurate diagnosis of coronary artery disease without manual intervention.

Contribution

It presents a novel bottom-up deep learning approach that predicts CAD-RADS scores using segment-wise analysis and multi-task learning, improving automation and accuracy.

Findings

Achieved AUC of 0.923 for identifying patients needing further investigation.

Achieved AUC of 0.914 for detecting coronary artery disease.

Demonstrated potential for fully-automated screening and diagnostic assistance.

Abstract

Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries. The method relies solely on a prior fully-automated centerline extraction and segment labeling and predicts the segment-wise stenosis degree and the overall calcification grade as auxiliary tasks in a multi-task learning setup. We evaluate our approach on a data collection consisting of 2,867 patients. On the task of identifying patients with a CAD-RADS score indicating the need for further invasive investigation our approach reaches an area under curve (AUC) of 0.923 and an AUC of 0.914 for determining whether the patient suffers from CAD. This level of performance enables our approach to be used in a fully-automated screening setup or to assist diagnostic CCTA reading, especially due to its neural architecture design -- which allows comprehensive predictions.