3-Dimensional optical scanning for body composition assessment: A 4-component model comparison of four commercially available scanners
Abstract
Background & aims Body composition assessment via 3-dimensional optical (3DO) scanning has emerged as a rapid and simple evaluation method. The aim of this study was to establish the precision of body composition estimates from four commercially available 3DO scanners and evaluate their validity as compared to a reference 4-component (4C) model. Methods The body composition of 171 participants was assessed using four commercially-available 3DO scanners (FIT3D®, Naked Labs®, Size Stream®, and Styku®) and a 4C model utilizing data from dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Body composition estimates were compared via equivalence testing, Deming regression, Bland-Altman analysis, concordance correlation coefficients (CCC), root mean square error (RMSE), and related metrics. Precision metrics, including the root mean square coefficient of variation (RMS-%CV), precision error, and intraclass correlation coefficient, were generated for duplicate scans in 139 participants. Results All scanners produced reasonably reliable estimates, with RMS-%CV of 2.3-4.3% for body fat percentage (BF%), 2.5-4.3% for fat mass (FM), and 0.7-1.4% for fat-free mass (FFM). ICC values ranged from 0.975 to 0.996 for BF% and 0.990 to 0.999 for FM and FFM. All scanners except Styku® demonstrated equivalence with 4C, using 5% equivalence regions, and constant errors of <1% for BF% and ≤0.5 kg for FM and FFM. However, the slopes of regression lines differed from the line of identity for most scanners and variables. CCC values ranged from 0.74 to 0.90 for BF%, 0.85 to 0.95 for FM, and 0.93 to 0.97 for FFM. RMSE values ranged from 3.7 to 6.1% for BF% and 2.8-4.6 kg for FM and FFM. Bland-Altman analysis indicated proportional bias of varying magnitudes was present for all scanners. Conclusions Commercially available 3DO scanners produce relatively reliable body composition estimates. Three out of four scanners demonstrated equivalence with a 4C model for assessments of BF%, FM, and FFM, although other metrics of validity varied among scanners, and proportional bias was present for all scanners.
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Whole-body optical scanners use digital imaging to estimate body volume, size, and circumferences at various anatomical locations to estimate body composition metrics.
“Whole-body optical scanners use digital imaging to estimate body volume, size, and circumferences at various anatomical locations to estimate body composition metrics [63].”
From The Utility of Body Composition Assessment in Nutrition and Clinical Practice: An Overview of Current Methodology (2021) · cites this paper as [63]
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- Authors
- Tinsley, G. M., Moore, M. L., Benavides, M. L., Dellinger, J. R., & Adamson, B. T.
- Affiliations
- Texas Tech University (US)
- Corresponding author
- Tinsley, Grant M.
- Venue
- Clinical Nutrition
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 39
- Issue
- 10
- Pages
- 3160-3167
- ISSN
- 0261-5614 (ISSN-L)
- OpenAlex
- W3006581297
- Cited by
- 66
- References
- 31
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 11 · 2023: 11 · 2022: 15 · 2021: 10 · 2020: 4
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Nutrition (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Texas Tech University
- Topics
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Health Sciences › Medicine › Surgery › Body Contouring and Surgery
- Keywords
- 3D optical scanning, body composition assessment, body fat percentage, fat mass, fat-free mass, measurement precision, proportional bias, dual-energy X-ray absorptiometry, air displacement plethysmography, bioimpedance spectroscopy
- MeSH terms
- Adolescent, Adult, Aged, Anthropometry, Body Composition, Cross-Sectional Studies, Equipment Design, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Plethysmography, Predictive Value of Tests, Reproducibility of Results, Absorptiometry, Photon, Electric Impedance, Imaging, Three-Dimensional, Young Adult, Optical Imaging
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