甲状腺人工智能模型的概括性和诊断性能 美国

WenWen Xu¹, XiaoHong Jia¹, ZiHan Mei¹+23

• ¹From the Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, 200025, Shanghai, China (W.W.X., X.H.J., Z.H.M., W.W.Z., T.L., H.T.Z., Y.J.D., J.Q.Z.); Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China (X.L.G., Y.L., C.C.F., K.Y.Z., Q.F., C.H.); Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (R.F.Z.); Department of Medical Ultrasound, Affiliated Hospital of Guizhou Medical University, Guiyang, China (Y.G., X.C.); Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China (X.M.L.); Department of Ultrasound, Yunnan Kungang Hospital, The Seventh Affiliated Hospital of Dali University, Anning, China (N.L.); Department of Ultrasound, Affiliated Hospital of Yan'an University, Yan'an, China (B.Y.B.); Department of Ultrasound, Tangdu Hospital, Fourth Military Medical University, Xi'an, China (Q.Y.L.); Department of Ultrasound, Shanxi Provincial People's Hospital, Taiyuan, China (J.P.Y.); Department of Ultrasound, Traditional Chinese Medical Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang Uygur Autonomous Region, China (H.Z.); Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou, China (L.G.); Department of Ultrasound, Jilin Central General Hospital, Jilin, China (B.G.); and College of Health Science and Technology, Shanghai Jiaotong University School of Medicine, Shanghai, China (J.Q.Z.).