放射科医生和深度学习对美国肝硬化等级的比较

Pedro Vianna1, Sara-Ivana Calce1, Pamela Boustros1

  • 1From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.).

Radiology
|October 3, 2023
PubMed
概括

深度学习模型在使用超声波图像对非酒精性脂肪性肝病 (NAFLD) 中的肝硬化症分级方面表现有前途. 这些人工智能工具为放射科医生提供了可比的诊断性能,有可能提高查准确性.