Novel body fat estimation using machine learning and 3-dimensional optical imaging

Patrick S. Harty, Breck Sieglinger, Steven B. Heymsfield, John A. Shepherd, David Bruner, Matthew T. Stratton, Grant M. Tinsley

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)842-845
Number of pages4
JournalEuropean Journal of Clinical Nutrition
Volume74
Issue number5
DOIs
StatePublished - May 1 2020

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