Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

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...

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Detalles Bibliográficos
Autores principales: Lin, Andrew, Manral, Nipun, McElhinney, Priscilla, Killekar, Aditya, Matsumoto, Hidenari, Kwiecinski, Jacek, Pieszko, Konrad, Razipour, Aryabod, Grodecki, Kajetan, Park, Caroline, Otaki, Yuka, Doris, Mhairi, Kwan, Alan C, Han, Donghee, Kuronuma, Keiichiro, Tomasino, Guadalupe Flores, Tzolos, Evangelos, Shanbhag, Aakash, Goeller, Markus, Marwan, Mohamed, Gransar, Heidi, Tamarappoo, Balaji K, Cadet, Sebastien, Achenbach, Stephan, Nicholls, Stephen J, Wong, Dennis T, Berman, Daniel S, Dweck, Marc, Newby, David E, Williams, Michelle C, Slomka, Piotr J, Dey, Damini
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047317/
https://www.ncbi.nlm.nih.gov/pubmed/35337643
http://dx.doi.org/10.1016/S2589-7500(22)00022-X

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047317/
https://www.ncbi.nlm.nih.gov/pubmed/35337643
http://dx.doi.org/10.1016/S2589-7500(22)00022-X

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