Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles
ML models, especially LightGBM and XGBoost, enhanced overall weight prediction but offered limited gains in identifying percentile-based risk. The Hadlock formula remains a strong tool for categorizing at-risk fetuses.
Authors
- Yariv Yogev
- Noam Shomron (ORCID: https://orcid.org/0000-0001-9913-6124)
- Ori Rottenstreich (ORCID: https://orcid.org/0000-0002-4064-1238)
- Misgav Rottenstreich (ORCID: https://orcid.org/0000-0003-4875-1455)
- Eran Ashwal (ORCID: https://orcid.org/0000-0003-3016-6282)
- M. Michael Cohen
- Omer Dor
Institutions
- Tel Aviv University (IL)
- Technion – Israel Institute of Technology (IL)
- Hebrew University of Jerusalem (IL)
- Tel Aviv Sourasky Medical Center (IL)
- McMaster University (CA)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-11-11
- DOI
- https://doi.org/10.1002/ijgo.70657
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00