United States, University of California, Los Angeles
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Visão Geral do Projeto
EasyGlucose is a cloud-powered, non-invasive, and cost-effective method of blood glucose monitoring for diabetic patients. Users first capture high-resolution images of their eye with their smartphone through a custom, low-cost anterior segment iris imaging adapter. A deep learning computer vision framework using convolutional neural networks developed with Azure Virtual Machines then analyzes iris morphological variation in the eye image to predict the patient's blood glucose level. EasyGlucose is incredibly accurate with an unprecedented error rate of 6.93%, significantly outperforming existing state-of-the-art non-invasive methods by over 30%. In addition, on the gold-standard Clarke Error Grid analysis, 100% of test predictions were given the highest possible evaluation of "clinically accurate" in Zone A. Since glucose levels are securely stored in the cloud with Azure SQL Database, patients can easily see long-term glucose trends to optimize their insulin treatments, and parents receive automated alerts if their children's glucose levels reach critical levels. With the offline sync feature of Azure Mobile App Services, EasyGlucose requires no internet connection, increasing platform portability and facilitating deployment in low-income and rural areas. No maintenance is required for EasyGlucose, unlike the calibration and replacement of test strips and sensors required by current invasive methods. Ultimately, by reducing the pain and cost of current methods, EasyGlucose provides a comfortable, user-friendly, non-invasive and accurate way for diabetic patients to manage their blood sugar levels at a fraction of the cost of today's methods.
Sobre a Equipe
Bryan is an undergraduate at UCLA studying Computer Science. He is interested in artificial intelligence, healthcare, and sustainability.