Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation, which suffer from low specificity and high interobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-driven diagnostic model to improve diagnostic accuracy and reduce variability.

Authors

  • Yi-Xin Li
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Yu Lu
    Faw-volkswagen Automative Co., Changchun, China.
  • Zhe-Ming Song
    Department of Gynecology, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, China.
  • Yu-Ting Shen
    Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China.
  • Wen Lu
    School of Sciences, Zhejiang SCI-TECH University, Hangzhou, Zhejiang 310000, China.
  • Min Ren
    Tianjin Cardiovascular Institute, Tianjin Chest Hospital, Tianjin, China.

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