用3D扫描和机器学习，低成本预测体脂、肌肉等健康指标。
Predicting Anthropometric Body Composition Variables Using 3D Optical Imaging and Machine Learning
- 用3D图像提取身高、体积等特征，结合半监督p-拉普拉斯回归模型。
- 仅用10%数据训练时，肌肉量误差约13%，骨密度约10%，体脂率约20%。
- 在数据少时表现优异，适合医疗中数据受限的场景。
准确预测附肢瘦体重（ALM）、体脂百分比（BFP）和骨矿物质密度（BMD）对慢性病早期诊断至关重要。目前依赖成本高、耗时长的双能X射线吸收测定法（DXA）测量。本文提出利用3D光学成像获取身高、体积、左小腿围等生物标志物，结合统计与机器学习模型替代DXA。数据集来自彭宁顿生物医学研究中心，包含847名患者。由于医疗数据采集存在技术与法律限制，且多数监督学习算法需大量数据，本文采用半监督p-拉普拉斯回归模型，首次将其应用于回归任务。当仅使用10%训练数据时，该模型对ALM、BMD、BFP的预测误差分别为约13%、10%、20%。在其他监督模型中，支持向量回归（SVR）在10%数据下对ALM和BMD的误差最低，约为8%；最小二乘SVR在80%数据下对BFP误差最低，约为11%。结果表明，p-拉普拉斯模型在数据受限环境下具有重要应用潜力。
原文摘要 · Abstract (English)
Accurate prediction of anthropometric body composition variables, such as Appendicular Lean Mass (ALM), Body Fat Percentage (BFP), and Bone Mineral Density (BMD), is essential for early diagnosis of several chronic diseases. Currently, researchers rely on Dual-Energy X-ray Absorptiometry (DXA) scans to measure these metrics; however, DXA scans are costly and time-consuming. This work proposes an alternative to DXA scans by applying statistical and machine learning models on biomarkers (height, volume, left calf circumference, etc) obtained from 3D optical images. The dataset consists of 847 patients and was sourced from Pennington Biomedical Research Center. Extracting patients' data in healthcare faces many technical challenges and legal restrictions. However, most supervised machine learning algorithms are inherently data-intensive, requiring a large amount of training data. To overcome these limitations, we implemented a semi-supervised model, the $p$-Laplacian regression model. This paper is the first to demonstrate the application of a $p$-Laplacian model for regression. Our $p$-Laplacian model yielded errors of $\sim13\%$ for ALM, $\sim10\%$ for BMD, and $\sim20\%$ for BFP when the training data accounted for 10 percent of all data. Among the supervised algorithms we implemented, Support Vector Regression (SVR) performed the best for ALM and BMD, yielding errors of $\sim 8\%$ for both, while Least Squares SVR performed the best for BFP with $\sim 11\%$ error when trained on 80 percent of the data. Our findings position the $p$-Laplacian model as a promising tool for healthcare applications, particularly in a data-constrained environment.
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