Maternal and fetal health status assessment by using machine learning on optical 3D body scans

Authors

Ruting Cheng, Department of Computer Science, The George Washington University, 800 22nd Street NW, Washington DC, 20052, USA. rcheng77@gwu.edu.
Yijiang Zheng, Department of Computer Science, The George Washington University, 800 22nd Street NW, Washington DC, 20052, USA.
Boyuan Feng, Department of Computer Science, The George Washington University, 800 22nd Street NW, Washington DC, 20052, USA.
Chuhui Qiu, Department of Computer Science, The George Washington University, 800 22nd Street NW, Washington DC, 20052, USA.
Zhuoxin Long, Department of Statistics, The George Washington University, 801 22nd Street NW, Washington DC, 20052, USA.
Joaquin A. Calderon, Department of Obstetrics and Gynecology, The George Washington University, 2150 Pennsylvania Ave. NW, Washington DC, 20037, USA.
Xiaoke Zhang, Department of Statistics, The George Washington University, 801 22nd Street NW, Washington DC, 20052, USA.
Jaclyn M. Phillips, Department of Obstetrics and Gynecology, The George Washington University, 2150 Pennsylvania Ave. NW, Washington DC, 20037, USA.
James K. Hahn, Department of Computer Science, The George Washington University, 800 22nd Street NW, Washington DC, 20052, USA.

Document Type

Journal Article

Publication Date

11-8-2025

Journal

Medical & biological engineering & computing

DOI

10.1007/s11517-025-03473-0

Keywords

3D body scan; Machine learning; Pregnancy outcomes; Telehealth

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 solutions 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 level circumference information, and the other for unsupervised learning to extract global shape descriptors, alongside a branch incorporating shape-related demographic data. Our results demonstrated that 3D body shapes can support the prediction of preterm labor and gestational diabetes mellitus (GDM), as well as the estimation of fetal weight. Compared to other machine learning models, our algorithm achieved the best performance, with prediction accuracies exceeding 89% and fetal weight estimation accuracy of 72.22% within a 10% error margin, outperforming the conventional anthropometric measurements-based method by 18.18%.

APA Citation

Cheng, Ruting; Zheng, Yijiang; Feng, Boyuan; Qiu, Chuhui; Long, Zhuoxin; Calderon, Joaquin A.; Zhang, Xiaoke; Phillips, Jaclyn M.; and Hahn, James K., "Maternal and fetal health status assessment by using machine learning on optical 3D body scans" (2025). GW Authored Works. Paper 8117.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/8117

Department

Obstetrics and Gynecology