Diabetes detection with a smartphone camera
Researchers at the University of California San Francisco (UCSF) have demonstrated how a popular smartphone application, designed to measure heart rate using the phone’s built-in camera, may help detect diabetes.
Even in its early stages, diabetes can cause changes to the blood vessels — the body’s fuel lines — and how blood flows through them, often referred to as vascular changes. These changes can be detected through screening tools such as glycated haemoglobin or fasting plasma glucose tests, which require a blood draw and an in-person clinic visit.
But many Americans don’t have easy access to a clinician, simply don’t go or can’t afford it. In addition, millions of people living with diabetes are undiagnosed, leaving them vulnerable to worsening outcomes and other health conditions, such as heart attack, heart failure and stroke.
“Diabetes can be asymptomatic for a long period of time, yet adverse vascular changes still occur silently, which can lead to cardiovascular complications,” said Robert Avram, a postdoctoral fellow at the UCSF Medical Center. “This makes it especially important for us to examine low-cost, non-invasive opportunities that make it easy to screen millions of people. To date, a non-invasive, widely scalable screening tool for diabetes has been lacking.”
Researchers at UCSF wanted to test whether diabetes could be detected using a smartphone camera and photoplethysmography (PPG) signal, which can pick up on vascular changes based on changes in blood flow. As noted by Dr Avram, “It’s a measurement that’s already readily obtained from smartphones and wearable devices to track heart rate.”
Dr Avram explained that variations in blood volume that occur with every heartbeat can be captured by shining a smartphone flashlight on a fingertip. With every heart contraction, blood pressure increases in the vessels causing them to expand, which then increases the amount of light reflected by the skin to the optical sensor of the phone’s camera. This input can then be converted to a waveform representing the volumetric change of the blood volume in a vessel.
With this in mind, Dr Avram and his colleagues studied 54,269 individuals enrolled in the online Health eHeart Study, who used the Azumio Instant Heart Rate smartphone app — one of the most widely downloaded apps to measure heart rate. The researchers developed and applied a deep learning algorithm that used the smartphone-based PPG signal recordings from participants to identify which patients had diabetes based on this signal alone.
Overall, the model correctly identified people suffering from diabetes in more than 72% of the cases using the PPG signal alone. The test had a strong negative predictive value of 97%, meaning that out of every 100 participants who were predicted as not having diabetes, 97 truly did not have the disease. When combining the diabetes score with other commonly accessible risk factors for diabetes, such as age, gender, body mass index and race/ethnicity, the ability to appropriately classify someone as having diabetes improved further, jumping to 81%.
“We’ve demonstrated that by using deep learning and a smartphone camera alone, we can … detect vascular changes associated with diabetes and with reasonable discrimination,” Dr Avram said. In fact, after coupling the app-based screening with common risk factors, researchers determined this tool is comparable to many traditional diabetes risk scores that are used in clinics to predict diabetes.
“We are hopeful this technology will assist with early diabetes detection,” Dr Avram said. “A positive screening test would still require a physician to confirm the diabetes diagnosis and establish appropriate treatment.”
Dr Avram will present the researchers’ findings at the American College of Cardiology’s 68th Annual Scientific Session, to be held in New Orleans from 16–18 March. He and his team are currently testing the new algorithm across two cardiovascular prevention clinics to further validate the findings.
The researchers also plan to evaluate how this technology performs in detecting whether someone has early- or late-stage diabetes and to validate it in different populations, such as African–Americans or Asians, which are underrepresented in this study’s cohort but have a higher chance of suffering from diabetes. They said once the model is validated in these populations, it will be available to consumers via a smartphone application, likely within the next two years.
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