Étude et rapport

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

GRATUIT

Auteur(s) :

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

Éditeur(s) :

THE LANCET

Date de publication :31/03/2022

10 pages

EN BREF ...

"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." En bref issu de l'étude.

Rédacteur(s) de la fiche : Beesens TEAM


Introductio

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Conclusio

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