Maternal and Fetal Health Status Assessment by Using Machine Learning on Optical 3D Body Scans

• URL: http://arxiv.org/abs/2504.05627v1
• Date: Tue, 08 Apr 2025 03:02:26 GMT
• Title: Maternal and Fetal Health Status Assessment by Using Machine Learning on Optical 3D Body Scans
• Authors: Ruting Cheng, Yijiang Zheng, Boyuan Feng, Chuhui Qiu, Zhuoxin Long, Joaquin A. Calderon, Xiaoke Zhang, Jaclyn M. Phillips, James K. Hahn,
• Abstract summary: This study explores the potential of 3D body scan data, captured during the 18-24 gestational weeks, to predict adverse pregnancy outcomes.<n>We developed a novel algorithm with two parallel streams which are used for extract body shape features.<n>Our results indicate that 3D body shape can assist in predicting preterm labor, gestational diabetes mellitus (GDM), gestational hypertension (GH) and in estimating fetal weight.
• Score: 3.153771294026575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monitoring maternal and fetal health during pregnancy is crucial for preventing adverse outcomes. While tests such as ultrasound scans offer high accuracy, they can be costly and inconvenient. Telehealth and more accessible body shape information provide pregnant women with a convenient way to monitor their health. This study explores the potential of 3D body scan data, captured during the 18-24 gestational weeks, to predict adverse pregnancy outcomes and estimate clinical parameters. We developed a novel algorithm with two parallel streams which are used for extract body shape features: one for supervised learning to extract sequential abdominal circumference information, and another for unsupervised learning to extract global shape descriptors, alongside a branch for demographic data. Our results indicate that 3D body shape can assist in predicting preterm labor, gestational diabetes mellitus (GDM), gestational hypertension (GH), and in estimating fetal weight. Compared to other machine learning models, our algorithm achieved the best performance, with prediction accuracies exceeding 88% and fetal weight estimation accuracy of 76.74% within a 10% error margin, outperforming conventional anthropometric methods by 22.22%.

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