Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.

Journal: Journal of minimally invasive gynecology
Published Date: May 19, 2025

Abstract

OBJECTIVE: To evaluate the predictive value of clinical features in the diagnosis of endometriosis by utilizing machine learning algorithms (MLAs), aiming to develop an accurate, explainable prediction model.

Authors

  • Danielle L Snyder
    College of Medicine, University of Florida.
  • Silvana Sidhom
    Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida.
  • Corinne E Chatham
    College of Medicine, University of Florida.
  • Sophie G Tillotson
    College of Medicine, University of Florida.
  • Ruben D Zapata
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida.
  • François Modave
    Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, USA.
  • Miranda Solly
    Department of Obstetrics & Gynecology, College of Medicine, University of Florida.
  • Amira Quevedo
    Department of Obstetrics and Gynecology, University of Louisville School of Medicine, Louisville, Kentucky.
  • Nash S Moawad
    Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, College of Medicine, University of Florida. Electronic address: nmoawad@ufl.edu.

Keywords

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