作者
Andrew Lin, Nipun Manral, Priscilla McElhinney, Aditya Killekar, Hidenari Matsumoto, Jacek Kwiecinski, Konrad Pieszko, Aryabod Razipour, Kajetan Grodecki, Caroline Park, Yuka Otaki, Mhairi Doris, Alan C Kwan, Donghee Han, Keiichiro Kuronuma, Guadalupe Flores Tomasino, Evangelos Tzolos, Aakash Shanbhag, Markus Goeller, Mohamed Marwan, Heidi Gransar, Balaji K Tamarappoo, Sebastien Cadet, Stephan Achenbach, Stephen J Nicholls, Dennis T Wong, Daniel S Berman, Marc Dweck, David E Newby, Michelle C Williams, Piotr J Slomka, Damini Dey
发表日期
2022/4/1
期刊
The Lancet Digital Health
卷号
4
期号
4
页码范围
e256-e265
出版商
Elsevier
简介
Background
Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity.
Methods
This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients …
学术搜索中的文章
A Lin, N Manral, P McElhinney, A Killekar… - The Lancet Digital Health, 2022