ReviewEuropean journal of clinical nutrition2023
Translating digital anthropometry measurements obtained from different 3D body image scanners.
Review in European journal of clinical nutrition, 2023. 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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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
6 citing papers in PubMed.
- Reference method matters: DXA system differences influence agreement with 3D optical imaging body composition estimates.International journal of obesity (2005) · 2026Article
- BodyMAE: A Surface-Area Aware Masked Autoencoder for Body Composition Estimation from 3D Body Scans.medRxiv : the preprint server for health sciences · 2026Article
- Updates on Methods for Body Composition Analysis: Implications for Clinical Practice.Current obesity reports · 2025Review
- BMI: Still Going Strong at Age 50.Pediatrics · 2024Article
- Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners.European journal of clinical nutrition · 2024Article
- Design of Innovative Clothing for Pressure Injury Prevention: End-User Evaluation in a Mixed-Methods Study.International journal of environmental research and public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundBody image scanners are used in industry and research to reliably provide a wealth of anthropometric measurements within seconds. The demonstrated utility of the scanners drives the current proliferation of more commercially available devices that rely on their own reference body sites and proprietary algorithms to output anthropometric measurements. Since each scanner relies on its own algorithms, measurements obtained from different scanners cannot directly be combined or compared.
objectivesTo develop mathematical models that translate anthropometric measurements between the three popular commercially available scanners.
methodsA unique database that contained 3D scanner measurements in the same individuals from three different scanners (Styku, Human Solutions, and Fit3D) was used to develop linear regression models that translate anthropometric measurements between each scanner. A limits of agreement analysis was performed between Fit3D and Styku against Human Solutions measurements and the coefficient of determination, bias, and 95% confidence interval were calculated. The models were then applied to normalized scanner data from four different studies to compare the results of a k-means cluster analysis between studies. A scree plot was used to determine the optimal number of clusters derived from each study.
resultsCorrelations ranged between R
conclusionsThe newly developed models that translate measurements between the scanners Styku and Fit3D to predict Human Solutions measurements make it possible to standardize data between scanners allowing for data pooling and comparison.
Indexed as
Identifiers
37165098What OpenQuestion holds
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.