Why the study?
Shoulder dystocia is a serious obstetric emergency associated with significant neonatal morbidity, prompting evaluation of machine learning models using fetal biometric ratios and clinical characteristics to predict it in pregnancies without suspected macrosomia.
Do machine learning models integrating fetal biometric ratios improve the prediction of shoulder dystocia in pregnancies without suspected macrosomia?
Population
284 women undergoing spontaneous vaginal delivery between 37 and 42 weeks with EFW below the 90th percentile
Comparison
84 shoulder dystocia cases vs 200 controls
Design
Retrospective case-control study
Authors
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May enhance ShD risk stratification in low-suspicion cases; leaves open prospective validation before clinical adoption.
Do machine learning models integrating fetal biometric ratios improve the prediction of shoulder dystocia in pregnancies without suspected macrosomia?
Machine learning models incorporating fetal biometric ratios can accurately predict shoulder dystocia in pregnancies without clinical suspicion of macrosomia.
Ulusoy et al. (2025) studied this question.
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