3D Convolutional Deep Learning for Nonlinear Estimation of Body Composition from Whole-Body Morphology.

ABSTRACT: Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST

SUBMITTER: Tian I 

PROVIDER: S-EPMC10896405 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature