1. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study
- Author
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Andrew Lin, MBBS, Nipun Manral, MSc, Priscilla McElhinney, BSc, Aditya Killekar, MSc, Hidenari Matsumoto, MD, Jacek Kwiecinski, MD, Konrad Pieszko, MD, Aryabod Razipour, MD, Kajetan Grodecki, MD, Caroline Park, BSc, Yuka Otaki, MD, Mhairi Doris, MD, Alan C Kwan, MD, Donghee Han, MD, Keiichiro Kuronuma, MD, Guadalupe Flores Tomasino, MD, Evangelos Tzolos, MD, Aakash Shanbhag, MSc, Markus Goeller, MD, Mohamed Marwan, MD, Heidi Gransar, MS, Balaji K Tamarappoo, MD, Sebastien Cadet, MSc, Stephan Achenbach, ProfMD, Stephen J Nicholls, ProfMBBS, Dennis T Wong, MBBS, Daniel S Berman, MD, Marc Dweck, ProfMBChB, David E Newby, ProfMD, Michelle C Williams, MBChB, Piotr J Slomka, ProfPhD, and Damini Dey, ProfPhD
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Summary: 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 (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. Findings: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p
- Published
- 2022
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