Estimating cardiometabolic risk with smartphone imagery

Insulin resistance classification

Next, we compared how well different combinations of data predicted insulin resistance, stacking our baseline demographics against combining it with standard tape measurements, smartwatch BIA sensors, PhotoScan, and gold-standard DXA scans.

We tested our models on the MetabolicMosaic cohort using a gradient boosting classifier to identify subjects with insulin resistance. To ensure our results were completely unbiased and leak-free, we implemented a rigorous testing process that repeatedly evaluated the model on unseen data. We also made sure each test group was evenly balanced by both BMI and insulin resistance status, ensuring a fair and realistic performance test. With this robust framework in place, we systematically fed the classifier five distinct feature sets to compare their predictive power, baseline demographics like age, sex, and body mass index, standard tape measure anthropometrics, smartwatch bioelectrical impedance, our smartphone PhotoScan metrics, and the clinical gold-standard DXA scans. By comparing how the model performed with each of these isolated inputs, we established the clinical value of our smartphone optical phenotyping.

To evaluate our models, we focused on two key metrics: the Area Under the Receiver Operating Characteristic curve (AUROC) and the Net Reclassification Index (NRI). Simply put, AUROC measures how accurately a model can distinguish between someone who has insulin resistance and someone who does not (higher is better). NRI, on the other hand, quantifies exactly how much our new digital metrics improve our ability to correctly categorize people compared to our old baseline model. As the figure below indicates, our baseline demographic model achieved an AUROC of 0.692. When we added the photoscan-based body composition features (demo + photoscan), the classification accuracy improved to an AUROC to 0.760 and NRI improved to 0.593, nearly as effective as using clinical DXA data itself, which topped out at an AUROC of 0.773 and an NRI of 0.748. In contrast, adding BIA with demographics (demo + bia below) yielded no improvement in AUROC or NRI for insulin resistance classification for IR classification, as BIA only provides BF% estimation, whose feature importance is significantly lower than A/G ratio and V/S ratio in the demo + photoscan model.