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 of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15 kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in R2s of 0.70-0.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition. CONCLUSIONS: This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.

Body composition, measured quantities of muscle, fat, and bone, is typically assessed through dual energy X-ray absorptiometry (DXA) scans, which requires specialized equipment, trained technicians and involves exposure to radiation. Exterior body shape is dependent on body composition and recent technological advances have made three-dimensional (3D) scanning for body shape accessible and virtually ubiquitous. We developed a model which uses 3D body surface scan inputs to generate DXA scans. When analyzed with commercial software that is used clinically, our model generated images yielded accurate quantities of fat, lean, and bone. Our work highlights the strong relationship between exterior body shape and interior composition. Moreover, it suggests that with enhanced accuracy, such medical imaging models could be more widely adopted in clinical care, making the analysis of body composition more accessible and easier to obtain.