3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39894882.
- Also identified by DOI 10.1038/s41746-025-01469-6 and PMC identifier 11788428.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Body composition prediction from 3D optical imagery has previously been studied with linear algorithms. In this study, we present a novel application of deep 3D convolutional graph networks and nonlinear Gaussian process regression for human body shape parameterization and body composition estimation. We trained and tested linear and nonlinear models with ablation studies on a novel ensemble body shape dataset containing 4286 scans. Nonlinear GPR produced up to a 20% reduction in prediction error and up to a 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only, and a 4-14% reduction in precision error for both sexes. All coefficients of determination (R<sup>2</sup>) for all predicted variables were above 0.86 and achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.