ArticleCommunications medicine2024
Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Paving the way for improved action: how nuclear techniques can advance the assessment of malnutrition.The American journal of clinical nutrition · 2025Article
- Advancing the Understanding and Treatment of Cancer Cachexia: Mechanisms, Therapeutic Approaches, and Future Opportunities.Cancer management and research · 2025Review
- Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling.International journal of obesity (2005) · 2024Article
- Prediction of total and regional body composition from 3D body shape.NPJ digital medicine · 2024Article
- Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.Research square · 2024Article
- Approximation of bone mineral density and subcutaneous adiposity using T1-weighted images of the human head.Imaging neuroscience (Cambridge, Mass.) · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundBody 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.
methodsOur 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.
resultsPredicted DXA scans achieve R
conclusionsThis work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.