Researchers developed PhotoScan, a deep learning system that estimates body composition metrics from smartphone photos to predict insulin resistance risk. The model was trained on 35,323 UK Biobank records and validated on 132 participants, achieving a body fat percentage error of 2.13% and an insulin resistance classification AUROC of 0.760, nearly matching the gold-standard DXA scan at 0.773. This smartphone-based approach offers a scalable alternative to expensive clinical imaging for early metabolic disease screening.
Whisker’s Litter-Robot 5 Pro uses AI-based cat identification and sensors to analyze litter-box deposits for individual cats’ usage and potential health flags.
It uses a dual camera system and an array of sensors.
As a result, the robot can differentiate between cats and provide cat-specific litter-box insights rather than treating all deposits as the same.
Viso Now launched as a self-building AI vision platform that converts images, video, and camera feeds into computer vision applications. It shows 86 followers on the listing. It removes the need for model training, annotation, or writing code by generating the vision logic and live dashboards automatically.
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