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Smartphone App And Deep Learning Help Detect Diabetes

August 18th, 2020 Scott Jung Cardiology, Diagnostics, Informatics, Medicine

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. So, they undertook a study as part of UCSF’s massive Health eHeart study to see if PPG could be an accurate biomarker to detect diabetes.

The researchers developed and validated a deep-learning algorithm using nearly 3 million PPG recordings from 53,870 patients. The algorithm impressively managed to correctly identify the presence of diabetes in 82% of patients with diabetes, as well as the absence of diabetes in 97% of patients without diabetes. The algorithm’s performance improved even further when combined with patient data, such as age, gender, and BMI, as well as other co-occurring comorbidities. An additional analysis even showed a correlation between the algorithm’s score and the subjects’ A1c.

While more research is needed on how to best incorporate this new digital biomarker, the results could establish PPG as a new modality in diabetes care and smartphones as an even more useful tool for our health.

Journal article in Nature: A digital biomarker of diabetes from smartphone-based vascular signals

More info: PPG Diabetes project website @ UCSF

Via: USCF

Scott Jung

Scott Jung (@scottjung) is a Silicon Valley-based medical and health technology reporter and advocate, with a focus on wearables, telemedicine, and health technology in emerging countries. He has represented Medgadget at CES and Digital Health Summit, TEDMED, Stanford Medicine X, and SXSW. Always on the lookout for innovative medical technology worldwide, Scott has been invited to visit Colombia, Poland, and many other countries around the world to share how medtech is enriching the lives of its people. Scott holds a B.S. degree in Biomedical Engineering from the University of Southern California and an M.S. degree in Medical Product Development Management from San José State University. Scott is always looking for the next big thing in medical technology and digital health. Interested in helping him transform lives? Get in touch with him at http://scottju.ng

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