用3D身体扫描数据预测妊娠并发症和胎儿体重，准确率超88%。
Maternal and Fetal Health Status Assessment by Using Machine Learning on Optical 3D Body Scans
- 双流网络分别提取腹部序列特征与全局体形信息
- 预测早产、妊娠糖尿病等准确率超88%，胎儿体重误差在10%内
- 适合远程孕检与可穿戴健康监测场景
孕期母婴健康监测对预防不良结局至关重要。尽管超声检查准确性高，但成本高且不便。远程医疗与更易获取的身体形态信息为孕妇提供了便捷的健康管理方式。本研究探索了在18-24孕周采集的3D身体扫描数据，用于预测不良妊娠结局并估计临床参数。我们提出一种新型双流算法：一路径基于监督学习提取腹部围度序列特征，另一路径通过无监督学习提取全局体形描述符，并整合人口学数据分支。结果表明，3D体形数据可辅助预测早产、妊娠期糖尿病（GDM）、妊娠期高血压（GH），并估算胎儿体重。相比其他机器学习模型，本方法表现最佳，预测准确率超过88%，胎儿体重估算在10%误差范围内的准确率达76.74%，较传统人体测量方法提升22.22%。
原文摘要 · Abstract (English)
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%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。