Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery

Researchers developed PhotoScan, a deep learning model that estimates detailed body composition metrics like body fat percentage and fat distribution ratios from standard 2D smartphone photos, aiming to assess cardiometabolic risk non-invasively. This approach could complement wearable data and traditional methods like DXA scans, offering accessible metabolic health insights.