Machine learning‐based prediction of large‐for‐gestational‐age neonates in diabetic and non‐diabetic pregnancies
A predictive algorithm, incorporating sonographic and non-sonographic features, as developed here, achieved superior accuracy compared to the traditional EFW formula in predicting LGA neonates, in both general and diabetic pregnant populations.
Authors
- Y. Gilboa (ORCID: https://orcid.org/0000-0002-5266-7769)
- Eran Hadar (ORCID: https://orcid.org/0000-0002-3101-2827)
- Ron Unger (ORCID: https://orcid.org/0000-0003-4153-3922)
- A. Romano
- Leor Perl (ORCID: https://orcid.org/0000-0002-0625-7937)
- Asnat Walfisch (ORCID: https://orcid.org/0000-0002-0201-9691)
- Nadav Loebl
- Ohad Houri
Institutions
- Hospital Sant Joan de Déu Barcelona (ES)
- Reichman University (IL)
- Bar-Ilan University (IL)
- Tel Aviv University (IL)
- Rabin Medical Center (IL)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1002/ijgo.70751
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00