ArticleClinical nutrition (Edinburgh, Scotland)2023
Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations.
Article in Clinical nutrition (Edinburgh, Scotland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 5 citations in OpenAlex.
- Better BMI: Novel Digital Anthropometry Equations for Adiposity Assessment in the Chinese Population.Phenomics (Cham, Switzerland) · 2026Article
- 3D body shape-based phenotyping for precision risk stratification of metabolic syndrome.Lipids in health and disease · 2026Article
- 3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology.NPJ digital medicine · 2025Article
- D3BT: Dynamic 3D Body Transformer for Body Fat Percentage Assessment.IEEE journal of biomedical and health informatics · 2025Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundExcess adiposity in children is strongly correlated with obesity-related metabolic disease in adulthood, including diabetes, cardiovascular disease, and 13 types of cancer. Despite the many long-term health risks of childhood obesity, body mass index (BMI) Z-score is typically the only adiposity marker used in pediatric studies and clinical applications. The effects of regional adiposity are not captured in a single scalar measurement, and their effects on short- and long-term metabolic health are largely unknown. However, clinicians and researchers rarely deploy gold-standard methods for measuring compartmental fat such as magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA) on children and adolescents due to cost or radiation concerns. Three-dimensional optical (3DO) scans are relatively inexpensive to obtain and use non-invasive and radiation-free imaging techniques to capture the external surface geometry of a patient's body. This 3D shape contains cues about the body composition that can be learned from a structured correlation between 3D body shape parameters and reference DXA scans obtained on a sample population. STUDY
aimThis study seeks to introduce a radiation-free, automated 3D optical imaging solution for monitoring body shape and composition in children aged 5-17.
methodsWe introduce an automated, linear learning method to predict total and regional body composition of children aged 5-17 from 3DO scans. We collected 145 male and 206 female 3DO scans on children between the ages of 5 and 17 with three scanners from independent manufacturers. We used an automated shape templating method first introduced on an adult population to fit a topologically consistent 60,000 vertex (60 k) mesh to 3DO scans of arbitrary scanning source and mesh topology. We constructed a parameterized body shape space using principal component analysis (PCA) and estimated a regression matrix between the shape parameters and their associated DXA measurements. We automatically fit scans of 30 male and 38 female participants from a held-out test set and predicted 12 body composition measurements.
resultsThe coefficient of determination (R
conclusionOptical imaging can quickly, safely, and inexpensively estimate regional body composition in children aged 5-17. Frequent repeat measurements can be taken to chart changes in body adiposity over time without risk of radiation overexposure.
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