Value of Artificial Intelligence in Improving the Accuracy of Diagnosing TI-RADS Category 4 Nodules.

Journal: Ultrasound in medicine & biology
Published Date: Aug 30, 2023

Abstract

OBJECTIVE: Considerable heterogeneity is observed in the malignancy rates of thyroid nodules classified as category 4 according to the Thyroid Imaging Reporting and Data System (TI-RADS). This study was aimed at comparing the diagnostic performance of artificial intelligence algorithms and radiologists with different experience levels in distinguishing benign and malignant TI-RADS 4 (TR4) nodules.

Authors

  • Min Lai
    Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China; Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China.
  • Bojian Feng
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Jincao Yao
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Yifan Wang
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Qianmeng Pan
    Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China.
  • Yuhang Chen
    School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China,People's Republic of China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Na Feng
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Fang Shi
    Capacity Building and Continuing Education Center of National Health Commission, Beijing, China.
  • Yuan Tian
    Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Lu Gao
    Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

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