Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA.
University of Hawaii Cancer Center, Honolulu, HI, USA.
Int J Obes (Lond). 2024 Dec;48(12):1741-1747. doi: 10.1038/s41366-024-01614-3. Epub 2024 Aug 24.
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 body composition analysis and 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 (%Δ ~ 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.
评估以下假设，即使用多元回归预测的人体三维（3D）人形化身的人体测量维度与使用地面真实 3D 光学成像方法获得的实际周长、体积和表面积测量值相比是准确的。如果这种方法预测的化身在人体测量维度上是准确的，那么它可以用于多种目的，包括患者身体成分分析和临床环境中的代谢疾病风险分层。
在完成 3D 光学扫描、双能 X 射线吸收法（DXA）和生物阻抗分析（BIA）评估的 570 名成年人样本中开发了多元回归 3D 化身预测方程。一个新的前瞻性 84 名成年人样本使用 20 个相机的 3D 参考扫描仪进行了 6 个身体周长、7 个体积和 7 个表面积的地面真实测量。在这些参与者中，使用包括年龄、体重、身高、DXA%脂肪和 BIA 阻抗在内的潜在预测变量的多元回归生成 3D 人形化身。使用相同的软件量化地面真实和预测化身的人体测量维度。
在进行探索性研究后，选择了一个包括年龄、体重、身高和%脂肪作为协变量的多元预测模型进行介绍。预测和地面真实化身具有相似的外观；预测和地面真实人体测量估计值之间的相关性均很高（Rs，0.75-0.99；均 P<0.001），除了手臂周长（%Δ~5%；P<0.05）外，无显著差异。一致性相关系数范围为 0.80-0.99，并且在 20 个总人体测量测量值中有 13 个存在小但显著的偏差（P<0.05-0.01），Bland-Altman 图显示。多元回归预测的腰围与臀围比与地面真实扫描仪测量值无显著差异。
无需 3D 扫描仪即可使用人口统计学、物理和其他可访问特征预测的 3D 化身可以生成具有准确人体测量维度的身体表示。将多元回归算法与 DXA、BIA 和其他可访问方法等现有的身体成分方法相结合，为新的研究和临床机会提供了可能性。
