ArticleMedical physics2022
A device-agnostic shape model for automated body composition estimates from 3D optical scans.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 22 citations in OpenAlex.
- Diagnosis, staging and management of obesity: a synthesis of contemporary guidelines.Nature reviews. Endocrinology · 2026Review
- Monitoring growth and body composition in children with 3-dimensional optical imaging: a pilot observational study.The American journal of clinical nutrition · 2026Observational
- Scanner-agnostic dynamic jaw motion generation from virtual static excursive records using open-source Python-based artificial intelligence (AI) interpolation.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- Maternal and fetal health status assessment by using machine learning on optical 3D body scans.Medical & biological engineering & computing · 2026Article
- Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.SN computer science · 2026Article
- 3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology.NPJ digital medicine · 2025Article
- D3BT: Dynamic 3D Body Transformer for Body Fat Percentage Assessment.IEEE journal of biomedical and health informatics · 2025Article
- Updates on Methods for Body Composition Analysis: Implications for Clinical Practice.Current obesity reports · 2025Review
- Prediction of total and regional body composition from 3D body shape.NPJ digital medicine · 2024Article
- Strengths and Limitations of BMI in the Diagnosis of Obesity: What is the Path Forward?Current obesity reports · 2024Review
- Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans.Communications medicine · 2024Article
- Equations for smartphone prediction of adiposity and appendicular lean mass in youth soccer players.Scientific reports · 2023Article
- Cross-sectional assessment of body composition and detection of malnutrition risk in participants with low body mass index and eating disorders using 3D optical surface scans.The American journal of clinical nutrition · 2023Article
- Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity.The American journal of clinical nutrition · 2023Observational
- Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations.Clinical nutrition (Edinburgh, Scotland) · 2023Article
- Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry.The American journal of clinical nutrition · 2023Article
- Machine learning-based obesity classification considering 3D body scanner measurements.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundMany predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods. PURPOSE: We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices.
methodsA fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52 kg/m
resultsWe achieved coefficients of determination (R
conclusionsOur method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.
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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.