Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

Journal: European radiology
Published Date: Feb 1, 2022

Abstract

PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint.

Authors

  • Liping Si
    Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China.
  • Jingyu Zhong
    Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China.
  • Jiayu Huo
  • Kai Xuan
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Huashan Road #1954, Shanghai, 200030, China.
  • Zixu Zhuang
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Huashan Road #1954, Shanghai, 200030, China.
  • Yangfan Hu
    Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China.
  • Qian Wang
    Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Huan Zhang
    Department of Plant Protection, Zhejiang University, 866 Yuhangtang Road, 5 Hangzhou 310058, China.
  • Weiwu Yao
    Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200336, China. yaoweiwuhuan@163.com.

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