Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.

导出 OA

翻译打印收藏纠错

作者： Benjamin,Peters ^[1] ; Jean-François,Paul ^[2] ; Rolf,Symons ^[3] ; Wouter M A,Franssen ^[4] ; Alain,Nchimi ^[5] ; Olivier,Ghekiere ^[6]

作者单位： Faculty of Medicine and Life Sciences, Hasselt University, LCRC, Agoralaan, Diepenbeek, 3590, Belgium. Benjamin.peters@jessazh.be.;Department of Radiology, Jessa Hospital, LCRC, Stadsomvaart 11, Hasselt, 3500, Belgium. Benjamin.peters@jessazh.be. ^[1] Department of Radiology, Institut Mutualiste Montsouris, 42 Boulevard Jourdan, Paris, France. ^[2] Department of Radiology, Imelda Hospital, Bonheiden, Belgium. ^[3] SMRC Sports Medical Research Center, BIOMED Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium. ^[4] GIGA Cardiovascular Sciences, Liège University (ULg), Domaine Universitaire du Sart Tilman, rue de l'Hôpital, Liège, Belgium.;Department of Radiology, Centre Hospitalier Universitaire, Luxembourg, Luxembourg, Luxembourg. ^[5] Faculty of Medicine and Life Sciences, Hasselt University, LCRC, Agoralaan, Diepenbeek, 3590, Belgium.;Department of Radiology, Jessa Hospital, LCRC, Stadsomvaart 11, Hasselt, 3500, Belgium. ^[6]

关键词 Artificial intelligence Coronary computed tomography angiography Deep learning Fractional flow reserve Intermediate coronary stenoses