ArticleSensors (Basel, Switzerland)2022
Digital Single-Image Smartphone Assessment of Total Body Fat and Abdominal Fat Using Machine Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Validity of non-traditional measures of obesity compared to total body fat across the life course: A systematic review and meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- What is measured and what is estimated in body composition? A clarification framework for terminology and interpretation.Reviews in endocrine & metabolic disorders · 2026Review
- Diagnosis, staging and management of obesity: a synthesis of contemporary guidelines.Nature reviews. Endocrinology · 2026Review
- Digital Solutions for the Control of Lifestyle Parameters in Individuals With High Cardiometabolic Risk: a Scoping Review.Current nutrition reports · 2026Article
- Advancing sarcopenia assessment with wearable and app-based technology: a scoping review.The journal of nutrition, health & aging · 2026Article
- Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method.NPJ digital medicine · 2025Article
- Updates on Methods for Body Composition Analysis: Implications for Clinical Practice.Current obesity reports · 2025Review
- Strengths and Limitations of BMI in the Diagnosis of Obesity: What is the Path Forward?Current obesity reports · 2024Review
- Childhood Obesity and Its Comorbidities in High-Risk Minority Populations: Prevalence, Prevention and Lifestyle Intervention Guidelines.Nutrients · 2024Review
- "Notification! You May Have Cancer." Could Smartphones and Wearables Help Detect Cancer Early?JMIR cancer · 2024Article
- Fluid-Dependent Single-Frequency Bioelectrical Impedance Fat Mass Estimates Compared to Digital Imaging and Dual X-ray Absorptiometry.Nutrients · 2023Article
- Smartphone-based abdominal inclination angles for assessing abdominal obesity: Convergent validity and reliability study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Obesity is chronic health problem. Screening for the obesity phenotype is limited by the availability of practical methods. Methods: We determined the reproducibility and accuracy of an automated machine-learning method using smartphone camera-enabled capture and analysis of single, two-dimensional (2D) standing lateral digital images to estimate fat mass (FM) compared to dual X-ray absorptiometry (DXA) in females and males. We also report the first model to predict abdominal FM using 2D digital images. Results: Gender-specific 2D estimates of FM were significantly correlated (p < 0.001) with DXA FM values and not different (p > 0.05). Reproducibility of FM estimates was very high (R2 = 0.99) with high concordance (R2 = 0.99) and low absolute pure error (0.114 to 0.116 kg) and percent error (1.3 and 3%). Bland−Altman plots revealed no proportional bias with limits of agreement of 4.9 to −4.3 kg and 3.9 to −4.9 kg for females and males, respectively. A novel 2D model to estimate abdominal (lumbar 2−5) FM produced high correlations (R2 = 0.99) and concordance (R2 = 0.99) compared to DXA abdominal FM values. Conclusions: A smartphone camera trained with machine learning and automated processing of 2D lateral standing digital images is an objective and valid method to estimate FM and, with proof of concept, to determine abdominal FM. It can facilitate practical identification of the obesity phenotype in adults.
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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.