Observational studyEuropean journal of clinical nutrition2026
Real-time and digital remote nutritional assessment framework with the use of smartphone-enabled facial morphometrics and machine learning- a proof of concept.
Observational study in European journal of clinical nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCurrent methods for assessing nutrition are often resource-intensive, requiring significant time, financial investment, and specialized equipment alongside clinical expertise.
objectiveThis research introduces an innovative approach that emphasizes accessible, scalable, and efficient digital solutions by leveraging facial morphometrics and machine learning to predict essential nutritional indicators.
methodsThe cross-sectional observational study involved 71 free-living Chinese adults (30 males, 41 females) aged 50-85. Utilizing widely accessible smartphone technology, 3D facial scans were employed to forecast nutritional metrics. The predictive performance of two machine-learning models, Random Forest (RF) and Extreme Gradient Boosting (XGB), was evaluated through ten-fold stratified cross-validation.
resultsThe RF model outperformed the XGB model, showing high predictive accuracy (median r² 0.51 to 0.92) for six parameters: muscle mass, basal metabolic rate (BMR), visceral fat index, appendicular skeletal muscle mass index, total body fat percentage, and hand grip strength. The highest predictive accuracy was found in muscle mass (r² = 0.92) and BMR (r² = 0.88) indicating strong correlations.
conclusionsThis non-invasive, economical technology presents a scalable approach to nutritional assessment with notable benefits for public health. The precise prediction of muscle mass and BMR facilitates efficient community-based screenings for undernutrition and frailty among older adults, while analysing body fat percentage aids in identifying overnutrition and related health risks. This digital approach shows significant potential for enhancing health outcomes on a population level through early detection and intervention.
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