Google's PhotoScan estimates body fat from smartphone photos

Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery

Google researchers have developed PhotoScan, a deep learning framework that estimates body composition metrics like body fat percentage and visceral-to-subcutaneous fat ratio from standard 2D smartphone photos. In an independent cohort, it achieved a mean absolute error of 2.13 for body fat percentage, outperforming smartwatch-based bioelectrical impedance analysis (MAE 2.91) and approaching the accuracy of DXA scans. The model also improved insulin resistance classification (AUROC 0.760) over demographics alone (0.692), nearly matching DXA (0.773).