用AI自动估算运动员体型数据，辅助心脏筛查
Automated Deep Learning Estimation of Anthropometric Measurements for Preparticipation Cardiovascular Screening
- 基于2D合成图像与深度学习回归模型，自动估测5项体型指标
- 最佳模型ResNet50平均误差仅0.668厘米，精度达亚厘米级
- 适合体育医疗筛查场景，可规模化替代人工测量
赛前心血管检查（PPCE）旨在通过识别结构性或电生理性心脏异常来预防运动员猝死（SCD）。腰围、肢体长度及躯干比例等人体测量指标可提示马凡综合征等高风险状况。传统人工测量耗时费力且依赖操作者，难以推广。本文提出一种全自动深度学习方法，从10万张由3D人体网格生成的2D合成图像中估计五项关键人体测量值。使用包含VGG19、ResNet50和DenseNet121的回归模型进行训练与评估，所有模型均达到亚厘米级精度，其中ResNet50表现最优，各项测量平均绝对误差（MAE）为0.668厘米。结果表明，深度学习可在大规模场景下提供高精度人体测量数据，为运动员筛查流程提供实用补充工具。未来工作将对真实图像进行验证以拓展应用范围。
原文摘要 · Abstract (English)
Preparticipation cardiovascular examination (PPCE) aims to prevent sudden cardiac death (SCD) by identifying athletes with structural or electrical cardiac abnormalities. Anthropometric measurements, such as waist circumference, limb lengths, and torso proportions to detect Marfan syndrome, can indicate elevated cardiovascular risk. Traditional manual methods are labor-intensive, operator-dependent, and challenging to scale. We present a fully automated deep-learning approach to estimate five key anthropometric measurements from 2D synthetic human body images. Using a dataset of 100,000 images derived from 3D body meshes, we trained and evaluated VGG19, ResNet50, and DenseNet121 with fully connected layers for regression. All models achieved sub-centimeter accuracy, with ResNet50 performing best, achieving a mean MAE of 0.668 cm across all measurements. Our results demonstrate that deep learning can deliver accurate anthropometric data at scale, offering a practical tool to complement athlete screening protocols. Future work will validate the models on real-world images to extend applicability.
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