MvBody: Multi-View-Based Hybrid Transformer Using Optical 3D Body Scan for Explainable Cesarean Section Prediction

• URL: http://arxiv.org/abs/2511.03212v1
• Date: Wed, 05 Nov 2025 06:02:48 GMT
• Title: MvBody: Multi-View-Based Hybrid Transformer Using Optical 3D Body Scan for Explainable Cesarean Section Prediction
• Authors: Ruting Cheng, Boyuan Feng, Yijiang Zheng, Chuhui Qiu, Aizierjiang Aiersilan, Joaquin A. Calderon, Wentao Zhao, Qing Pan, James K. Hahn,
• Abstract summary: We propose a novel multi-view-based Transformer network, MvBody, which predicts cesarean section risk using only self-reported medical data and 3D optical body scans.<n>Compared to widely used machine learning models and the latest advanced 3D analysis methods, our method demonstrates superior performance.<n>Our results indicate that pre-pregnancy weight, maternal age, obstetric history, previous CS history, and body shape, particularly around the head and shoulders, are key contributors to CS risk prediction.
• Score: 12.529565733654783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately assessing the risk of cesarean section (CS) delivery is critical, especially in settings with limited medical resources, where access to healthcare is often restricted. Early and reliable risk prediction allows better-informed prenatal care decisions and can improve maternal and neonatal outcomes. However, most existing predictive models are tailored for in-hospital use during labor and rely on parameters that are often unavailable in resource-limited or home-based settings. In this study, we conduct a pilot investigation to examine the feasibility of using 3D body shape for CS risk assessment for future applications with more affordable general devices. We propose a novel multi-view-based Transformer network, MvBody, which predicts CS risk using only self-reported medical data and 3D optical body scans obtained between the 31st and 38th weeks of gestation. To enhance training efficiency and model generalizability in data-scarce environments, we incorporate a metric learning loss into the network. Compared to widely used machine learning models and the latest advanced 3D analysis methods, our method demonstrates superior performance, achieving an accuracy of 84.62% and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.724 on the independent test set. To improve transparency and trust in the model's predictions, we apply the Integrated Gradients algorithm to provide theoretically grounded explanations of the model's decision-making process. Our results indicate that pre-pregnancy weight, maternal age, obstetric history, previous CS history, and body shape, particularly around the head and shoulders, are key contributors to CS risk prediction.

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