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

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.

Trial registration: ClinicalTrials.gov NCT03637855.

Conflict of interest statement

Conflict of Interest

Size Stream, LLC, the manufacturer of the 3D scanner utilized in the present study, supported the project through equipment loan/donation to the study sites. However, monetary funding was not provided to the study sites or investigators as part of this project. Size Stream, LLC was also involved in the study design, execution, analysis, and interpretation of the study results. D.B. is employed by Size Stream, LLC, and B.S. is a paid analysis consultant for Size Stream, LLC. S.B.H. is on the medical advisory board for Tanita Medical. J.A.S. has received in-kind support from Size Stream, LLC, Styku, and FIT3D and has received research funding from Hologic and General Electric (GE) Healthcare. G.M.T. has received in-kind support from Size Stream, LLC, Naked Labs Inc., RJL Systems, MuscleSound, and Biospace, Inc. (DBA InBody). The remaining authors declare no potential conflicts of interest.

Figures

Figure 1.. Decision Tree Analysis.

Machine learning (i.e., decision tree analysis) identified lower abdomen circumference (AC) as the primary anthropometric factor that distinguished between higher and lower 4-component model body fat percentage, with a criterion separation point of 103.5 cm (40.75 inches). The decision tree procedures further produced the two body fat percentage equations displayed in the manuscript. Abbreviations. AC: lower abdominal circumference in cm, ATI: appendage-to-trunk index, BSA: body surface area in cm2, TC: thigh circumference (right) in cm

Figure 2.. Validation of 3DO Body Fat Estimates.

New 3DO body fat estimates were developed using paired 3DO and 4C assessments (panel A). The 3DO estimates were externally cross validated against DXA. Cross-validation indicated a small deviation from the slope of the line of identity (panel B; slope: 0.90 [95% confidence interval: 0.83, 0.98]), but no proportional bias (panel C; slope: 0.03 [95% confidence interval: −0.05, 0.10]). Abbreviations. 3DO: 3-dimensional optical imaging, 4C: 4-component model, DXA: dual-energy x-ray absorptiometry, LOA: limits of agreement