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A noninvasive digital biomarker is being developed at the University of California, San Francisco (UCSF) for detecting Type 2 diabetes using a smartphone camera and deep learning algorithm. This innovation could provide a low-cost, in-home alternative to blood draws and clinic-based screening tools.
The researchers hypothesized that a smartphone camera could be used to detect vascular damage due to diabetes by measuring photoplethysmography (PPG) signals, which most mobile devices, including smartwatches and fitness trackers, are capable of acquiring. They developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based PPG signals. The smartphone flashlight and camera measured PPGs by capturing color changes…READ MORE