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Purpose <p>Shoulder dystocia (SD) is a serious delivery complication, often occurring without identifiable risk factors. Accurate risk assessment is essential for educated decisions regarding the mode of delivery. This study aimed to develop and validate a machine learning-based model for SD prediction.</p> Methods <p>We conducted a retrospective analysis of term singleton vaginal deliveries at a single academic hospital over 10&#xa0;years. Exclusion criteria included cesarean deliveries and multiple gestations. Maternal and fetal characteristics were compared between SD and non-SD cases. Eleven features were selected for model development. Missing values in normally distributed variables were imputed using mean values. To address class imbalance, repeated random sampling of non-SD cases was performed. Data were split into training and test sets (70:30) and standardized. Multiple machine learning models—including logistic regression, decision tree, random forest, support vector machine, XGBoost, and CatBoost—were evaluated using cross-validation and area under the ROC curve (AUC).</p> Results <p>Among 51,628 deliveries, 94 (0.18%) involved SD. SD was associated with higher BMI, shorter stature, and increased diabetes rates (all <i>p</i> &lt; 0.05). Mean birthweight was significantly higher in the SD group (3751&#xa0;g vs 3287&#xa0;g, <i>p</i> &lt; 0.01). The CatBoost model achieved the highest performance (AUC = 0.83, 95% CI 0.77–0.89). Key predictive features were sonographic estimated fetal weight (EFW, 55.6% of the model’s decision-making weight), maternal BMI (20.1%), and clinical EFW (7.9%).</p> Conclusion <p>Our machine learning-based model predicted SD with an AUC of 0.83 and may support clinicians in delivery planning.</p>

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Antepartum prediction of shoulder dystocia using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月31日

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  • Lior Heresco,
  • Noa Levy,
  • Omer Todress,
  • Hadar Gluska,
  • Tal Biron-Shental,
  • Omer Weitzner

摘要

Purpose

Shoulder dystocia (SD) is a serious delivery complication, often occurring without identifiable risk factors. Accurate risk assessment is essential for educated decisions regarding the mode of delivery. This study aimed to develop and validate a machine learning-based model for SD prediction.

Methods

We conducted a retrospective analysis of term singleton vaginal deliveries at a single academic hospital over 10 years. Exclusion criteria included cesarean deliveries and multiple gestations. Maternal and fetal characteristics were compared between SD and non-SD cases. Eleven features were selected for model development. Missing values in normally distributed variables were imputed using mean values. To address class imbalance, repeated random sampling of non-SD cases was performed. Data were split into training and test sets (70:30) and standardized. Multiple machine learning models—including logistic regression, decision tree, random forest, support vector machine, XGBoost, and CatBoost—were evaluated using cross-validation and area under the ROC curve (AUC).

Results

Among 51,628 deliveries, 94 (0.18%) involved SD. SD was associated with higher BMI, shorter stature, and increased diabetes rates (all p < 0.05). Mean birthweight was significantly higher in the SD group (3751 g vs 3287 g, p < 0.01). The CatBoost model achieved the highest performance (AUC = 0.83, 95% CI 0.77–0.89). Key predictive features were sonographic estimated fetal weight (EFW, 55.6% of the model’s decision-making weight), maternal BMI (20.1%), and clinical EFW (7.9%).

Conclusion

Our machine learning-based model predicted SD with an AUC of 0.83 and may support clinicians in delivery planning.