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This paper is in the following e-collection/theme issue:

Information Models (95) Clinical Information and Decision Making (3455) Decision Support for Health Professionals (2074) Machine Learning (2992) Surgical Treatment Planning (24) Applications of AI (808)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76021, first published .
Tablet screen showing Postoperative Stress Urinary Incontinence prediction results.

Predicting Postoperative Stress Urinary Incontinence After Prolapse Surgery via Machine Learning and Regression Models: Development and Validation Study

Predicting Postoperative Stress Urinary Incontinence After Prolapse Surgery via Machine Learning and Regression Models: Development and Validation Study

Authors of this article:

Minna Su1 Author Orcid Image ;   Shuyu Wang1 Author Orcid Image ;   Xiaochun Liu1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Katorkin S, Kolsanova A, Katorkina E. Methods of artificial intelligence, three-dimensional and finite element modeling in the diagnosis of pelvic organ prolapse based on visualization. Meditsinskiy sovet = Medical Council 2026;20(5):282 View
  2. Scheiner D, Betschart C, Perucchini D. E-Health in der Urogynäkologie. Ein Essay. Journal für Gynäkologische Endokrinologie/Schweiz 2026 View