Machine learning-based prediction of large-for-gestational-age neonates in diabetic and non-diabetic pregnancies.

Journal: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Published Date: Dec 22, 2025

Abstract

OBJECTIVE: This study determines whether a machine-learning model integrating sonographic biometry with maternal clinical parameters improves prediction of large-for-gestational-age (LGA) compared with Hadlock's EFW formula. METHODS: We conducted a retrospective cohort study including all singleton live births at ≥32 gestational weeks at a tertiary medical center. Predictors comprised biparietal diameter, abdominal circumference, femur length, maternal demographics and anthropometrics, obstetric history, chronic and gestational morbidity, and glucose values from screening and diagnosis. A CatBoost gradient-boosting model estimated the probability of LGA (birthweight ≥90th percentile). Performance was compared with Hadlock's EFW using area under the curve (AUC) and detection at a fixed 10% false-positive rate. A prespecified subgroup analysis evaluated pregnancies with pregestational or gestational diabetes. Performance was assessed with fivefold cross-validation; calibration and utility were examined by decision curve analysis. RESULTS: Among 31 531 parturients, 18.17% delivered an LGA neonate. The model achieved an AUC of 0.946 (95% confidence interval [CI], 0.938-0.955), significantly outperforming Hadlock's EFW (AUC 0.867; 95% CI, 0.854-0.881; P = 0.01) and yielding a higher detection rate at a 10% false-positive rate (79% vs. 63%). The most influential contributors were abdominal circumference, gestational age at delivery, fetal sex, and maternal age. In 3871 diabetic pregnancies, among whom 24% delivered LGA, performance remained high (AUC 0.890; 95% CI, 0.847-0.918) and exceeded Hadlock's formula (AUC 0.820; 95% CI, 0.772-0.863; P = 0.02). CONCLUSION: 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

  • Ohad Houri
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
  • Asaf Romano
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
  • Asnat Walfisch
    Division of Obstetrics & Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Eran Hadar
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
  • Yinon Gilboa
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
  • Leor Perl
    Department of Cardiology, Rabin Medical Center, Petah Tikvah, Israel.
  • Nadav Loebl
    Beilinson Medical Center Innovation, Artificial Intelligence Center, Rabin Medical Center, Petah Tikva, Israel, Faculty of Computer Science, Reichman University, Herzliya, Israel.
  • Ron Unger
    Roni Shouval, Hila Mishan-Shamay, Avichai Shimoni, and Arnon Nagler, The Chaim Sheba Medical Center, Tel-Hashomer; Roni Shouval, Ori Bondi, and Ron Unger, Bar-Ilan University, Ramat-Gan, Israel; Myriam Labopin, Norbert C. Gorin, Emmanuelle Polge, Arnon Nagler, and Mohamad Mohty, European Group for Blood and Marrow Transplantation; Myriam Labopin and Mohamad Mohty, Sorbonne Universités, Centre de Recherche (CDR) Saint-Antoine; Myriam Labopin and Mohamad Mohty, Institut National de la Santé et de la Recherche Médicale, CDR Saint-Antoine; Myriam Labopin and Mohamad Mohty, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Antoine, Paris, France; Fabio Ciceri, San Raffaele Scientific Institute, Milan; Andrea Bacigalupo, Ospedale San Martino, Genoa, Italy; Jordi Esteve, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain; Sebastian Giebel, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland; Christoph Schmid, Ludwig-Maximilians-University, Munich; Nicolaus Kroger, University Medical Center Hamburg Eppendorf, Hamburg, Germany; Mahmoud Aljurf, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia; Charles Craddock, Queen Elizabeth Hospital, Birmingham, United Kingdom; Jan J. Cornelissen, Erasmus University Medical Center, Rotterdam, the Netherlands; and Frederic Baron, University of Liège, Liège, Belgium.

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