TY - JOUR
T1 - Novel body fat estimation using machine learning and 3-dimensional optical imaging
AU - Harty, Patrick S.
AU - Sieglinger, Breck
AU - Heymsfield, Steven B.
AU - Shepherd, John A.
AU - Bruner, David
AU - Stratton, Matthew T.
AU - Tinsley, Grant M.
N1 - Funding Information:
Grant M. Tinsley grant.tinsley@ttu.edu
Publisher Copyright:
© 2020, Springer Nature Limited.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n = 158), producing a R2 value of 0.78 and a constant error of (X ± SD) 0.8 ± 4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland–Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.
AB - Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n = 158), producing a R2 value of 0.78 and a constant error of (X ± SD) 0.8 ± 4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland–Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.
UR - http://www.scopus.com/inward/record.url?scp=85081904577&partnerID=8YFLogxK
U2 - 10.1038/s41430-020-0603-x
DO - 10.1038/s41430-020-0603-x
M3 - Article
C2 - 32203233
AN - SCOPUS:85081904577
VL - 74
SP - 842
EP - 845
JO - European Journal of Clinical Nutrition
JF - European Journal of Clinical Nutrition
SN - 0954-3007
IS - 5
ER -