Heymsfield Steven, McCarthy Cassidy, Wong Michael, Brown Jasmine, Ramirez Sophia, Yang Shengping, Bennett Jonathan, Shepherd John
Pennington Biomedical Research Center.
Pennington Biomedical.
Res Sq. 2024 Jul 13:rs.3.rs-4565498. doi: 10.21203/rs.3.rs-4565498/v1.
To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings.
Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.
Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (Rs, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.
3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.
评估一种假设，即与通过地面真值三维（3D）光学成像方法获取的实际周长、体积和表面积测量值相比，从人的多变量回归预测的三维（3D）人形化身得出的人体测量尺寸是准确的。如果使用这种方法预测的化身在人体测量尺寸方面准确，则可用于多种目的，包括临床环境中患者代谢疾病风险分层。
在570名完成3D光学扫描、双能X线吸收法（DXA）和生物电阻抗分析（BIA）评估的成年人样本上开发多变量回归3D化身预测方程。一个由84名成年人组成的新的前瞻性样本使用20台相机的3D参考扫描仪对6个身体周长、7个体积和7个表面积进行了地面真值测量。在这些参与者身上生成了3D人形化身，采用多变量回归，将年龄、体重、身高、DXA脂肪百分比和BIA阻抗作为潜在预测变量。使用相同软件对地面真值和预测化身的人体测量尺寸进行量化。
经过探索性研究，提出了一个多变量预测模型，该模型将年龄、体重、身高和脂肪百分比作为协变量。预测化身和地面真值化身具有相似的视觉外观；预测和地面真值人体测量估计值之间的相关性都很高（Rs，0.75 - 0.99；所有p < 0.001），除了手臂周长（%D ~ 5%；p < 0.05）外，平均差异不显著。一致性相关系数范围为0.80 - 0.99，在总共20项人体测量中有13项的Bland-Altman图显示存在小但显著的偏差（p < 0.05 - 0.01）。多变量回归预测的平均腰臀围比与地面真值扫描仪测量值无显著差异。
根据人口统计学、身体特征和其他可获取特征预测的3D化身无需3D扫描仪即可生成具有准确人体测量尺寸的身体表征。将多变量回归算法与DXA、BIA等既定身体成分分析方法以及其他可获取方法相结合，提供了新的研究和临床机会。
