3D Body shape for regional and appendicular body composition estimation.
3D Body shape for regional and appendicular body composition estimation.
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3D 身体形状，用于估计区域和四肢身体成分。
DOI：
10.1117/12.2653964
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hahn,JamesK
中科院分区：
文献类型：
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作者：
Zheng,Yijiang;Long,Zhuoxin;Zhang,Xiaoke;Hahn,JamesK
身体成分与骨矿物质密度、肌肉力量和身体表现相关。这对于诊断像肌肉减少症这样的疾病很重要，肌肉减少症被定义为与年龄相关的肌肉质量减少，导致移动的功能降低，虚弱增加和不平衡。现有的身体成分测量方法要么结果不准确，要么需要昂贵的设备，如双能X射线吸收法（DXA）。虽然DXA测量的是瘦体重而不是肌肉质量，但以前的研究认为四肢瘦体重是近似的骨骼肌质量（ASMM）。在这项研究中，我们开发了一个新的形状描述符来预测区域的身体组成（特别是区域瘦体重）从三维身体形状。此外，我们提出了一个神经网络的ASMM评估，这是由瘦体重计算。我们通过比较调整后的R平方值和均方根误差（RMSE）来评估有效性。在我们的实验中，利用水平周长作为训练特征的回归模型优于所有区域人体测量值，并将平均RMSE降低约21%。对于ASMM，所提出的神经网络，它结合了形状特征和人口统计特征，超过了所有其他传统的回归模型，并达到最低的RMSE为1.85公斤。与普通线性回归模型相比，我们的方法将RMSE提高了17%。实验结果表明，3D身体形状有可能被用来预测身体组成，特别是瘦体重，为整个身体以及身体的特定区域。
Body composition is correlated to bone mineral density, muscle strength, and physical performance. This is important for diagnosing conditions like sarcopenia, which is defined as the age-associated decrease in muscle mass leading to decreased mobile function, increased frailty, and imbalance. Existing methods for body composition measurement either suffer from inaccurate results or require expensive equipment such as Dual-energy x-ray absorptiometry (DXA). Although DXA measures lean mass and not muscle mass, previous studies have considered extremity lean mass as appendicular skeletal muscle mass (ASMM) approximation. In this study, we develop a new shape descriptor to predict regional body composition (in particular, regional lean mass) from 3D body shapes. In addition, we propose a neural network for ASMM assessment which is calculated by lean mass. We evaluate the effectiveness by comparing adjusted R-Squared values and Root Mean Square Error (RMSE). In our experiment, the regression models utilizing level circumference as the training feature outperforms all regional anthropometric measurements and lowers the average RMSE by about 21%. For ASMM, the proposed neural network, which combines shape features and demographic features, surpasses all other traditional regression models and reaches the lowest RMSE at 1.85 kg. Compared to the vanilla linear regression model, our approach improves the RMSE by 17%. The experimental results suggest that the 3D body shape has the potential to be used to predict body composition, and in particular lean mass, for the whole body as well as specific regions of the body.