Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA

Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category.

According to the conclusions, deep convolutional neural network (CNN) yielded accurate automated Coronary Artery Disease Reporting and Data System (CAD-RADS) classification in patients with suspected CAD; CAD-RADS classification is significantly faster compared to human evaluation; and CNN can reduce the time of CCTA reporting in the next future.

Reference

1. Muscogiuri G, Chiesa M, Trotta M, Gatti M, Palmisano V, Dell'Aversana S, Baessato F, Cavaliere A, Cicala G, Loffreno A, Rizzon G, Guglielmo M, Baggiano A, Fusini L, Saba L, Andreini D, Pepi M, Rabbat MG, Guaricci AI, De Cecco CN, Colombo G, Pontone G. Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA. Atherosclerosis 2019 Dec 23;294:25-32. doi: 10.1016/j.atherosclerosis.2019.12.001. Go to PubMed