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

Saved in:
Bibliographic Details
Title: Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.
Authors: Snyder DL; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida., Sidhom S; Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida (Dr. Sidhom), Gainesville, Florida., Chatham CE; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida., Tillotson SG; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida., Zapata RD; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida (Dr. Zapata), Gainesville, Florida., Modave F; Department of Anesthesiology, University of Florida (Dr. Modave), Gainesville, Florida; Department of Pediatrics, Center for Remote Health Monitoring, Center for AI Research, School of Medicine, Wake Forest University (Dr. Modave), Winston-Salem, North Carolina., Solly M; Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Dr. Solly), Gainesville, Florida., Quevedo A; Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Drs. Quevedo and Moawad), Gainesville, Florida., Moawad NS; Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Drs. Quevedo and Moawad), Gainesville, Florida. Electronic address: nmoawad@ufl.edu.
Source: Journal of minimally invasive gynecology [J Minim Invasive Gynecol] 2025 Sep; Vol. 32 (9), pp. 784-792.e12. Date of Electronic Publication: 2025 May 19.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 101235322 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1553-4669 (Electronic) Linking ISSN: 15534650 NLM ISO Abbreviation: J Minim Invasive Gynecol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1553-4669
DOI:10.1016/j.jmig.2025.05.003