RESUMEN
BACKGROUND/OBJECTIVES: Body composition and anthropometry assessment from two-dimensional smartphone images is possible through advancement of computational hardware and artificial intelligence (AI) techniques. This study established agreement of a novel smartphone assessment, compared with traditional bioelectrical impedance analysis (BIA), and criterion measures. SUBJECTS/METHODS: Body composition of 929 adults was measured using DXA (GE lunar iDXA), a foot-to-foot BIA machine (TANITA BC-313), and predictions from two-dimensional smartphone images. Anthropometry measures were also collected. Body composition and anthropometry estimates were compared via concordance coefficient correlation (CCC), equivalence testing, Bland-Altman analysis, and root mean square error (RMSE). RESULTS: 2D smartphone image predictions for percent body fat (%BF) (males: CCC = 0.90 and RMSE = 2.9, and females: CCC = 0.90 and RMSE = 2.8) reported greater agreement with DXA measures than the BIA measures (males: CCC = 0.66 and RMSE = 5.6, and females: CCC = 0.79 and RMSE = 4.6). All anthropometry 2D smartphone image predictions had a strong agreement with criterion measurements (CCC ≥ 0.84 and RMSE ≤ 3.3). Body composition and anthropometry measures predicted by the 2D smartphone images were clinically equivalent at ±2.5 and ±5.0% thresholds. BIA %BF was not equivalent at either threshold; with only female BIA fat-free mass equivalent at the ±5% threshold. CONCLUSION: Body composition predictions from 2D smartphone application images provide a promising alternative to BIA scales for in-home body composition assessment. Future research should assess the validity of this method for longitudinally tracking body composition and indicating an individual's potential risk of chronic diseases.