ReviewEuropean journal of clinical nutrition2019
A machine learning approach relating 3D body scans to body composition in humans.
Review in European journal of clinical nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Essential dataset features in a successful obesity registry: a systematic review.International health · 2025Pooled it
- Automatic classification of female body shape using 3D anthropometric scan data.Scientific reports · 2026Article
- 3D body shape-based phenotyping for precision risk stratification of metabolic syndrome.Lipids in health and disease · 2026Article
- Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.SN computer science · 2026Article
- Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.Nature communications · 2025Review
- D3BT: Dynamic 3D Body Transformer for Body Fat Percentage Assessment.IEEE journal of biomedical and health informatics · 2025Article
- Editorial: Body composition assessment and future disease risk.Frontiers in medicine · 2025Article
- Optimal Instruments for Measurement of Dietary Intake, Physical Activity, and Sleep Among Adults in Population-Based Studies: Report of a National Heart, Lung, and Blood Institute Workshop.Journal of the American Heart Association · 2024Review
- Combination of Machine Learning Techniques to Predict Overweight/Obesity in Adults.Journal of personalized medicine · 2024Article
- Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners.European journal of clinical nutrition · 2024Article
- Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry.Obesity pillars · 2024Article
- Phenomic Imaging.Phenomics (Cham, Switzerland) · 2023Review
- Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study.European journal of clinical nutrition · 2023Article
- Translating digital anthropometry measurements obtained from different 3D body image scanners.European journal of clinical nutrition · 2023Review
- Machine learning-based obesity classification considering 3D body scanner measurements.Scientific reports · 2023Article
- Article
- How shape-based anthropometry can complement traditional anthropometric techniques: a cross-sectional study.Scientific reports · 2020Article
- Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.Sensors (Basel, Switzerland) · 2020Article
- Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men.PloS one · 2020Article
- The aging human body shape.NPJ aging and mechanisms of disease · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
A long-standing question in nutrition and obesity research involves quantifying the relationship between body fat and anthropometry. To date, the mathematical formulation of these relationships has relied on pairing easily obtained anthropometric measurements such as the body mass index (BMI), waist circumference, or hip circumference to body fat. Recent advances in 3D body shape imaging technology provides a new opportunity for quickly and accurately obtaining hundreds of anthropometric measurements within seconds, however, there does not yet exist a large diverse database that pairs these measurements to body fat. Herein, we leverage 3D scanned anthropometry obtained from a population of United States Army basic training recruits to derive four subpopulations of homogenous body shape archetypes using a combined principal components and cluster analysis. While the Army database was large and diverse, it did not have body composition measurements. Therefore, these body shape archetypes were paired to an alternate smaller sample of participants from the Pennington Biomedical Research Center in Baton Rouge, LA that were not only similarly imaged by the same 3D scanning machine, but also had concomitant measures of body composition by dual-energy X-ray absorptiometry body composition. With this enhanced ability to obtain anthropometry through 3D scanning quickly of large populations, our machine learning approach for pairing body shapes from large datasets to smaller datasets that also contain state-of-the-art body composition measurements can be extended to pair other health outcomes to 3D body shape anthropometry.
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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.