Peer-Reviewed Publication
JACC Adv2024;3(9):100861.September 1, 2024Journal Article
Coronary Artery Stenosis and High-Risk Plaque Assessed With an Unsupervised Fully Automated Deep Learning Technique.
Abdul Rahman Ihdayhid1, Amro Sehly2, Albert He2, Jack Joyner3, Julien Flack3, John Konstantopoulos3, David E Newby4, Michelle C Williams4, Brian S Ko5, Benjamin J W Chow6, Girish Dwivedi7
1Fiona Stanley Hospital, Perth, Australia; Artrya Ltd, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia; Curtin University, Perth, Australia. Electronic address: abdul.ihdayhid@perkins.org.au.
2Fiona Stanley Hospital, Perth, Australia.
3Artrya Ltd, Perth, Australia.
4The University of Edinburgh, Edinburgh, Scotland.
5Monash University, Melbourne, Australia.
6University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
7Fiona Stanley Hospital, Perth, Australia; Artrya Ltd, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia; University of Western Australia, Perth, Australia. Electronic address: girish.dwivedi@perkins.uwa.edu.au.
Abstract
BACKGROUND: Coronary computed tomography angiography (CCTA) has emerged as a reliable noninvasive modality to assess coronary artery stenosis and high-risk plaque (HRP). However, CCTA assessment of stenosis and HRP is time-consuming and requires specialized training, limiting its clinical translation.
OBJECTIVES: The aim of this study is to develop and validate a fully automated deep learning sys…
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