Diabetes is one of the world’s top causes of disease and death, affecting more than 450 million people worldwide. While technology has come a long way in helping to detect and manage diabetes, it still typically involves blood draws and clinical tools. Moreover, around half of all people with diabetes aren’t even aware that they have the disease.
Researchers at UC San Francisco have now come up with a promising method of detecting diabetes using a smartphone camera and some deep learning, utilizing the publicly available Instant Heart Rate app from Azumio to capture photoplethysmography (PPG) measurements. When a user places his or her fingertip over the phone’s flashlight and camera, the app measures PPG’s by capturing color changes in the fingertip corresponding to each heartbeat. This data is reported back to the user as the instantaneous heart rate.
While heart rate measurement might be an impressive use of a smartphone in itself, the UCSF researchers found that the heart rate data, along with heart rate variability and certain features of the PPG waveform morphology, correlate with many physiological mechanisms affected by diabetes, such as endothelial aging and microvascular atherosclerosis. Read more
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