Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations.

Abstract

<h4>Background</h4>Excess 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.<h4>Study aim</h4>This study seeks to introduce a radiation-free, automated 3D optical imaging solution for monitoring body shape and composition in children aged 5-17.<h4>Methods</h4>We 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.<h4>Results</h4>The coefficient of determination (R²) between 3DO predicted body composition and DXA measurements was at least 0.85 for all measurements with the exception of visceral fat on 3D scan predictions. Precision error was 1-4 times larger than that of DXA. No predicted variable was significantly different from DXA measurement except for male trunk lean mass.<h4>Conclusion</h4>Optical 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.