Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery
Google researchers introduce PhotoScan, a deep learning tool that uses smartphone imagery to estimate body composition and predict insulin resistance. The study suggests this method could provide a non-invasive alternative to clinical scans for assessing metabolic health.
Why it matters
It represents a significant advancement in using consumer-grade hardware for preventative healthcare and early disease detection.
Home Blog Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery August 17, 2026
Cassie Zhou, Research Scientist, and Ahmed Metwally, Staff Research Scientist, Google Research
We demonstrate the feasibility of PhotoScan, a deep learning approach estimating body composition from smartphone photos, to predict insulin resistance with accuracy comparable to DXA scans in a clinical research setting.
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