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Michael C. Wong
Postdoctoral Researcher (T32, completed)
Michael was a T32 postdoctoral researcher in the Shepherd Research Lab focused on the analysis of “Shape Up!” optical-imaging data. His background is in nutritional sciences.
He has since completed the T32 fellowship — see his LinkedIn (above) for his current position.
Publications (31)
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3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology
Tian IY, Liu J, Wong MC, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
npj Digital Medicine, 2025· doi:10.1038/s41746-025-01469-6 -
Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling
McCarthy C, Wong MC, Brown J, Ramirez S, Yang S, Bennett JP, Shepherd J, Heymsfield SB
International Journal of Obesity, 2024· doi:10.1038/s41366-024-01614-3 -
Evaluation of body shape as a human body composition assessment in isolated conditions and remote environments
Wong MC, Bennett JP, Leong LT, Liu YE, Kelly NN, Cherry JA, Kloza K, Li B, Iuliano S, Sibonga JD, Sawyer A, Ayton J, Shepherd J
npj Microgravity, 2024· doi:10.1038/s41526-024-00412-5 -
Trunk-to-leg volume and appendicular lean mass from a commercial 3-dimensional optical body scanner for disease risk identification
Bennett JP, Wong MC, Liu YE, Quon BK, Kelly NN, Garber AK, Heymsfield SB, Shepherd J
Clinical Nutrition, 2024· doi:10.1016/j.clnu.2024.09.028 -
Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans
Leong LT, Wong MC, Liu YE, Glaser Y, Quon BK, Kelly NN, Cataldi D, Sadowski P, Heymsfield SB, Shepherd J
Communications Medicine, 2024· doi:10.1038/s43856-024-00434-w -
Predictors of visceral and subcutaneous adipose tissue and muscle density: The ShapeUp! Kids study
Maskarinec G, Shvetsov YB, Wong MC, Cataldi D, Bennett JP, Garber AK, Buchthal SD, Heymsfield SB, Shepherd J
Nutrition Metabolism and Cardiovascular Diseases, 2023· doi:10.1016/j.numecd.2023.12.014 -
Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations
Tian IY, Wong MC, Nguyen WM, Kennedy S, McCarthy C, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.07.012 -
Cross-sectional assessment of body composition and detection of malnutrition risk in participants with low body mass index and eating disorders using 3D optical surface scans
Garber AK, Bennett JP, Wong MC, Tian IY, Maskarinec G, Kennedy S, McCarthy C, Kelly NN, Liu YE, Machen VI, Heymsfield SB, Shepherd J
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.08.004 -
Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study
Cataldi D, Bennett JP, Wong MC, Quon BK, Liu YE, Kelly NN, Kelly TL, Schoeller DA, Heymsfield SB, Shepherd J
Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.11.040 -
Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity
Wong MC, Bennett JP, Quon BK, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow DC, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd J
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.07.010 -
Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry
Wong MC, Bennett JP, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Wong JM, Ebbeling CB, Ludwig DS, Irving BA, Scott MC, Stampley J, Davis B, Johannsen NM, Matthews R, Vincellette C, Garber AK, ...
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.02.006 -
Smartphone prediction of skeletal muscle mass: model development and validation in adults
McCarthy C, Tinsley GM, Yang S, Irving BA, Wong MC, Bennett JP, Shepherd J, Heymsfield SB
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.02.003 -
Emergence of the adolescent obesity epidemic in the United States: five-decade visualization with humanoid avatars
Bennett JP, Wong MC, McCarthy C, Fearnbach N, Queen K, Shepherd J, Heymsfield SB
International Journal of Obesity, 2022· doi:10.1038/s41366-022-01153-9 -
What Is a 2021 Reference Body?
Müller MJ, Bosy‐Westphal A, Braun W, Wong MC, Shepherd J, Heymsfield SB
Nutrients, 2022· doi:10.3390/nu14071526 -
Emergence of the obesity epidemic: 6-decade visualization with humanoid avatars
Wong MC, McCarthy C, Fearnbach N, Yang S, Shepherd J, Heymsfield SB
American Journal of Clinical Nutrition, 2022· doi:10.1093/ajcn/nqac005 -
A device‐agnostic shape model for automated body composition estimates from 3D optical scans
Tian IY, Wong MC, Kennedy S, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
Medical Physics, 2022· doi:10.1002/mp.15843 -
Three‐dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults
Bennett JP, Liu YE, Quon BK, Kelly NN, Leong LT, Wong MC, Kennedy S, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, Shepherd J
Obesity, 2022· doi:10.1002/oby.23470 -
Subcutaneous and visceral fat assessment by DXA and MRI in older adults and children
Maskarinec G, Shvetsov YB, Wong MC, Garber AK, Monroe KR, Ernst T, Buchthal SD, Lim U, Marchand LL, Heymsfield SB, Shepherd J
Obesity, 2022· doi:10.1002/oby.23381 -
Next-generation smart watches to estimate whole-body composition using bioimpedance analysis: accuracy and precision in a diverse, multiethnic sample
Bennett JP, Liu YE, Kelly NN, Quon BK, Wong MC, McCarthy C, Heymsfield SB, Shepherd J
American Journal of Clinical Nutrition, 2022· doi:10.1093/ajcn/nqac200 -
Anthropometric evaluation of a 3D scanning mobile application
Smith B, McCarthy C, Dechenaud M, Wong MC, Shepherd J, Heymsfield SB
Obesity, 2022· doi:10.1002/oby.23434 -
Multicomponent density models for body composition: Review of the dual energy X‐ray absorptiometry volume approach
Heymsfield SB, Smith B, Wong MC, Bennett JP, Ebbeling CB, Wong JM, Strauss BJ, Shepherd J
Obesity Reviews, 2021· doi:10.1111/obr.13274 -
Digital anthropometric evaluation of young children: comparison to results acquired with conventional anthropometry
Kennedy S, Smith B, Sobhiyeh S, Dechenaud M, Wong MC, Kelly NN, Shepherd J, Heymsfield SB
European Journal of Clinical Nutrition, 2021· doi:10.1038/s41430-021-00938-x -
A pose‐independent method for accurate and precise body composition from 3D optical scans
Wong MC, Ng BK, Tian IY, Sobhiyeh S, Pagano I, Dechenaud M, Kennedy S, Liu YE, Kelly NN, Chow DC, Garber AK, Maskarinec G, Pujades S, Black MJ, Curless B, Heymsfield SB, Shepherd J
Obesity, 2021· doi:10.1002/oby.23256 -
Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner
Bennett JP, Liu YE, Quon BK, Kelly NN, Wong MC, Kennedy S, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, Shepherd J
Clinical Nutrition, 2021· doi:10.1016/j.clnu.2021.11.031 -
Predictors of liver fat among children and adolescents from five different ethnic groups
Maskarinec G, Garber AK, Wong MC, Kelly NN, Kazemi L, Buchthal SD, Fearnbach N, Heymsfield SB, Shepherd J
Obesity Science & Practice, 2020· doi:10.1002/osp4.459