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Chenchen Dai1, Ying Xiong1, Pingyi Zhu1

  • 1From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.).

Radiology
|May 14, 2024
PubMed
概括

一个新的深度学习算法在CT扫描上准确地识别良性小质,匹配放射科医生的性能. 这种人工智能工具有助于精确表征病变,包括小于1厘米的小病变.