ArticleEuropean journal of clinical nutrition2024
Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass.
Article in European journal of clinical nutrition, 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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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.
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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, 5 citations in OpenAlex.
- From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Review
- Reference Methods for Measuring Skeletal Muscle Mass: A Critical Perspective.Journal of cachexia, sarcopenia and muscle · 2026Review
- Predicting anthropometric body composition variables using 3D optical imaging and machine learning.Frontiers in bioinformatics · 2026Article
- Visual demonstration of weight loss and health risk improvement with a dual GIP and GLP-1 receptor agonist.International journal of obesity (2005) · 2025Article
- Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model.Journal of functional morphology and kinesiology · 2025Article
- Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling.International journal of obesity (2005) · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Currently available anthropometric body composition prediction equations were often developed on small participant samples, included only several measured predictor variables, or were prepared using conventional statistical regression methods. Machine learning approaches are increasingly publicly available and have key advantages over statistical modeling methods when developing prediction algorithms on large datasets with multiple complex covariates. This study aimed to test the feasibility of predicting DXA-measured appendicular lean mass (ALM) with a neural network (NN) algorithm developed on a sample of 576 participants using 10 demographic (sex, age, 7 ethnic groupings) and 43 anthropometric dimensions generated with a 3D optical scanner. NN-predicted and measured ALM were highly correlated (n = 116; R
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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.