The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets.

Journal: Diabetes & metabolic syndrome
PMID: 37907027

Abstract

BACKGROUND AND AIMS: It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets.

Authors

  • Jin Xu
    Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, and School of Statistics, East China Normal University, Shanghai, China.
  • He-Li Xu
    Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Yi-Ning Cao
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China; Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Ying Huang
    Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Southwest Medical University Luzhou, Sichuan, China.
  • Song Gao
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Qi-Jun Wu
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China; Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China; Key Laboratory of Reproductive and Genetic Medicine (China Medical University), National Health Commission, Shenyang, China. Electronic address: wuqj@sj-hospital.org.
  • Ting-Ting Gong
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China. Electronic address: gongtt@sj-hospital.org.

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