Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans.

ABSTRACT:

Background

Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics.

Methods

Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy.

Results

Predicted DXA scans achieve R²

SUBMITTER: Leong LT 

PROVIDER: S-EPMC10824755 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature