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Clinical Information and Decision Making (3890) Obstetrics, Gynecology, and Reproductive Medicine (433) Decision Support for Health Professionals (2348) Machine Learning (3337) Artificial Intelligence (5021) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (177)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70068, first published .
Pregnant Asian woman resting on a couch, holding her belly

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study

Authors of this article:

Qing Hua1 Author Orcid Image ;   Fengchun Yang2, 3 Author Orcid Image ;   Yadan Zhou1 Author Orcid Image ;   Fenglian Shi1 Author Orcid Image ;   Xiaoyan You1 Author Orcid Image ;   Jing Guo1 Author Orcid Image ;   Li Li1 Author Orcid Image
Article Authors Cited by (14) Tweetations Metrics

Journals

  1. Hoyos W, García R, Aguilar J. Multistage Training of Fuzzy Cognitive Maps to Predict Preeclampsia and Fetal Growth Restriction. IEEE Access 2025;13:136779 View
  2. Gao Z, Luo L, Han Y, Sun Y, Huang Y, Li S, Chen X, Yang H, Peng Z, Wang X, Zhao W, Wu X, Wu H, Bai J, Sun W, Zhou L, Ba Y. Serum Pepsinogen as a Biomarker of Gastrointestinal Stromal Tumors (GIST) in Stomach. Cancer Medicine 2025;14(17) View
  3. Ługowski F, Babińska J, Stanirowski P. Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review. Frontiers in Medicine 2026;12 View
  4. Li N, Shu S, Wang M, Xu S, Huang X, Li B. A nomogram for predicting fetal growth restriction in patients with preeclampsia based on complete blood count results at 20–24 weeks of gestation: a retrospective case–control study in China. BMC Pregnancy and Childbirth 2026;26(1) View
  5. Andonotopo W, Bachnas M, Pramono M, Dewantiningrum J, Sanjaya I, Darmawan E, Akbar M, Aldiansyah D, Yeni C, Bernolian N, Andanaputra W, Stanojevic M. Artificial Intelligence in Perinatal Medicine: A Systematic Review of Current Applications, Limitations, and a Translational Roadmap for the Foundation-Model Era. Dr. Sulaiman Al Habib Medical Journal 2025;7(4):214 View
  6. Wu Y. Machine Learning‐Based Risk Prediction Models for Pregnancy‐Related Syndromes. Birth Defects Research 2026;118(3) View
  7. Andonotopo W, Bachnas M, Dewantiningrum J, Pramono M, Sanjaya I, Darmawan E, Aldiansyah D, Stanojevic M, Kurjak A. Reimagining Placental Perfusion in Preeclampsia: Integrating Doppler Ultrasound, Three-dimensional Vascular Indices, and Predictive Artificial Intelligence. Journal of Medical Ultrasound 2026;34(1):22 View
  8. Hua Q, You X, Yan X, Xin Y, Zhang D, Tian X, Li L. SLC6A8-centered placental molecular signature predicts fetal growth restriction via machine learning and single-cell analysis. Reproductive Biology 2026;26(3):101234 View
  9. Xie J, Shi C, Dou L, Peng D, Li L, Xu R, Wu Y. Machine learning for precision obstetrics: A comprehensive review of risk prediction models for hypertensive disorders and pregnancy-related syndromes. Pregnancy Hypertension 2026;45:101506 View
  10. Tong T, Gu H, Lyu H. Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network. Abdominal Radiology 2026 View
  11. He X, Hu R, Deng X, Yang F. Current Applications and Advances of Ultrasound in Predicting Adverse Pregnancy Outcomes in Pregnant Women with Pre-eclampsia. Advanced Ultrasound in Diagnosis and Therapy 2026;10(3):148 View
  12. Gurugubelli K, Ballambattu V, Karra S. Artificial Intelligence in Prediction and Diagnosis of Fetal Growth Restriction: A Systematic Review. Indian Journal of Pediatrics 2026 View
  13. Benuzzi M, Kawakita T, Monari F, Melis B, Guariglia G, La Marca A, Neri I, Saade G, Facchinetti F, El Moudden I. Limits of Machine Learning for Predicting Stillbirth at Term: A Population-Based Cohort of 302,192 Pregnancies in Northern Italy. American Journal of Obstetrics & Gynecology MFM 2026:102148 View

Conference Proceedings

  1. Molla M, Sojib S, Akter M, Rahman M, Khan M, Habib M. 2026 International Conference on Machine Learning and Autonomous Systems (ICMLAS). Safety-Enhanced Machine Learning for Fetal Health Classification Using Cardiotocography Data View