作者
Giuseppe Muscogiuri, Mattia Chiesa, Michela Trotta, Marco Gatti, Vitanio Palmisano, Serena Dell’Aversana, Francesca Baessato, Annachiara Cavaliere, Gloria Cicala, Antonella Loffreno, Giulia Rizzon, Marco Guglielmo, Andrea Baggiano, Laura Fusini, Luca Saba, Daniele Andreini, Mauro Pepi, Mark G Rabbat, Andrea I Guaricci, Carlo N De Cecco, Gualtiero Colombo, Gianluca Pontone
发表日期
2020/2/1
期刊
Atherosclerosis
卷号
294
页码范围
25-32
出版商
Elsevier
简介
Background and aims
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.
Methods
Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1–2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs …
学术搜索中的文章
G Muscogiuri, M Chiesa, M Trotta, M Gatti, V Palmisano… - Atherosclerosis, 2020