Michael C Wong1,2, Bennett K Ng3, Isaac Tian4, Sima Sobhiyeh5, Ian Pagano2, Marcelline Dechenaud5, Samantha F Kennedy5, Yong E Liu2, Nisa N Kelly2, Dominic Chow6, Andrea K Garber7, Gertraud Maskarinec2, Sergi Pujades8, Michael J Black9, Brian Curless4, Steven B Heymsfield5, John A Shepherd1,2. 1. Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, Hawaii, USA. 2. Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA. 3. Department of Emerging Growth and Incubation, Intel Corp., Santa Clara, California, USA. 4. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA. 5. Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA. 6. John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawaii, USA. 7. School of Medicine, University of California, San Francisco, California, USA. 8. Inria, Université Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France. 9. Max Planck Institute for Intelligent Systems, Tübingen, Germany.
Abstract
OBJECTIVE: The aim of this study was to investigate whether digitally re-posing three-dimensional optical (3DO) whole-body scans to a standardized pose would improve body composition accuracy and precision regardless of the initial pose. METHODS: Healthy adults (n = 540), stratified by sex, BMI, and age, completed whole-body 3DO and dual-energy X-ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low-dimensional space by principal component analysis (stratified by sex). The total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs). RESULTS: The analysis included 472 participants after exclusions. After re-posing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared with DXA was as follows: fat mass R2 = 0.91 male, 0.94 female; fat-free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 0.77 male, 0.79 female. CONCLUSIONS: Re-posed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted.
OBJECTIVE: The aim of this study was to investigate whether digitally re-posing three-dimensional optical (3DO) whole-body scans to a standardized pose would improve body composition accuracy and precision regardless of the initial pose. METHODS: Healthy adults (n = 540), stratified by sex, BMI, and age, completed whole-body 3DO and dual-energy X-ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low-dimensional space by principal component analysis (stratified by sex). The total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs). RESULTS: The analysis included 472 participants after exclusions. After re-posing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared with DXA was as follows: fat mass R2 = 0.91 male, 0.94 female; fat-free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 0.77 male, 0.79 female. CONCLUSIONS: Re-posed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted.
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