Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study.

Journal: Journal of ovarian research
PMID: 39506832

Abstract

OBJECTIVES: The study aimed to compare the diagnostic efficacy of the machine learning models with expert subjective assessment (SA) in assessing the malignancy risk of ovarian tumors using transvaginal ultrasound (TVUS).

Authors

  • Xin He
    Department of Nephrology, The Affiliated Hospital of Guizhou Medical, Guizhou, China.
  • Xiang-Hui Bai
    From the Department of Obstetrics and Gynecology (H.C., B.W.Y., L.Q., X.H., M.J.J., Q.W.D., W.W.F.) and Department of Pathology (F.Y.), Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin 2nd Road, Huangpu District, Shanghai 200025, China; and Philips Research Asia Shanghai, Shanghai, China (Y.S.M., X.H.B., X.W.H.).
  • Hui Chen
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.
  • Wei-Wei Feng
    From the Department of Obstetrics and Gynecology (H.C., B.W.Y., L.Q., X.H., M.J.J., Q.W.D., W.W.F.) and Department of Pathology (F.Y.), Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin 2nd Road, Huangpu District, Shanghai 200025, China; and Philips Research Asia Shanghai, Shanghai, China (Y.S.M., X.H.B., X.W.H.).

Keywords

External Resources

Popular Topics
Recent Journals