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

Viewpoints and Perspectives (619) Artificial Intelligence (2129) Clinical Information and Decision Making (1915) Machine Learning (2076) Decision Support for Health Professionals (1539) Obstetrics, Gynecology, and Reproductive Medicine (329)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28120, first published .
Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation

Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation

Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation

Authors of this article:

Joshua Guedalia1 Author Orcid Image ;   Michal Lipschuetz1, 2 Author Orcid Image ;   Sarah M Cohen2 Author Orcid Image ;   Yishai Sompolinsky2 Author Orcid Image ;   Asnat Walfisch2 Author Orcid Image ;   Eyal Sheiner3 Author Orcid Image ;   Ruslan Sergienko4 Author Orcid Image ;   Joshua Rosenbloom2 Author Orcid Image ;   Ron Unger1 Author Orcid Image ;   Simcha Yagel2 Author Orcid Image ;   Hila Hochler2 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. Michalitsi K, Metallinou D, Diamanti A, Georgakopoulou V, Kagkouras I, Tsoukala E, Sarantaki A. Artificial Intelligence in Predicting the Mode of Delivery: A Systematic Review. Cureus 2024 View
  2. Maor G, Shapira Z, Bar C, Moore S, Yagur Y, Biron-Shental T, Weitzner O. Emergent cesarean section during active labor—does cervical dilatation matter?. Archives of Gynecology and Obstetrics 2024;310(6):2915 View
  3. Bai J, Kang X, Wang W, Yang Z, Ou W, Huang Y, Lu Y. A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system. DIGITAL HEALTH 2024;10 View
  4. Huang X, Di X, Lin S, Yao M, Zheng S, Liu S, Lau W, Ye Z, Wang Z, Liu B. Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysis. eClinicalMedicine 2025;80:103072 View
  5. AlSaad R, Farrell T, Dela Cruz J, Abd-Alrazaq A, Thomas R, Sheikh J. Artificial intelligence models for predicting the mode of delivery in maternal care. Journal of Gynecology Obstetrics and Human Reproduction 2025;54(7):102976 View
  6. Givon I, Bor N, Matot R, Friedrich L, Gross D, Konforty G, Benis A, Hadar E. Dynamic machine learning models for predicting cesarean delivery risk in women with no prior cesarean delivery: A retrospective nationwide cohort analysis. International Journal of Gynecology & Obstetrics 2025;171(2):775 View
  7. Sindhu P, Rao P. Latent feature discovery with kernel-PCA and random forest for childbirth method classification. International Journal of Information Technology 2025;17(8):4659 View
  8. Zhou Y, Li J, Hou X, Li Z, Xu Y, Wang Y, Sun M, Zheng F, Guo E, Zhou J. Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions. Clinical and Experimental Obstetrics & Gynecology 2025;52(7) View
  9. Sompolinsky Y, Guedalia J, Vilk-Ayalon N, Cohen S, Greenbaum S, Kabiri D, Yagel S, Lipschuetz M. Fetal head station during artificial rupture of membranes: a large retrospective cohort study. Frontiers in Medicine 2025;12 View