Deep learning for intelligent diagnosis in thyroid scintigraphy.

Journal: The Journal of international medical research
PMID:

Abstract

OBJECTIVE: To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy.

Authors

  • Tingting Qiao
    Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
  • Simin Liu
    College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
  • Zhijun Cui
    Department of Medicine Imaging, the Chongming Branch of Shanghai Tenth People's Hospital, Tongji University, Shanghai, China.
  • Xiaqing Yu
    Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
  • Haidong Cai
    Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
  • Huijuan Zhang
    Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, 14260, United States.
  • Ming Sun
    Department of Urology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China.
  • Zhongwei Lv
    Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
  • Dan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China.