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

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 View