Out of the 422 million people around the world living with diabetes, one in three of them will develop diabetic retinopathy (DR), a common condition that can lead to permanent blindness if left untreated. While early detection and treatment can dramatically reduce that risk, a third of people with diabetes have never even been screened for DR, as many are living in low-income, medically underserved areas that make the much-needed clinical intervention impossible.
But new research from IBM suggests technology can rise up to fill those healthcare gaps. Using a mix of deep learning, convolutional neural networks and visual analytics technology based on 35,000 images accessed via EyePACs, the IBM technology learned to identify lesions and other markers of damage to the retina’s blood vessels, collectively assessing the presence and severity of disease. In just 20 seconds, the method was successful in classifying DR severity with 86 percent accuracy, suggesting doctors and clinicians could use the technology to have a better idea of how the disease progresses as well as identify effective treatment methods.
While it’s still early days for the technology, researchers are excited for the possibility of using IBM’s method to replace or augment traditional DR screening and treatment processes, which currently require a comprehensive in-person visit with a specialist.
“A comprehensive, dilated eye exam is required in order to identify DR and many other eye diseases, and that can’t be done on a smart phone with the current camera technology in existence today. Where a smart phone could play a role is also in the cloud delivery of results to care teams and to patients – for example, you could use your smart phone to pull test results down from the cloud via any browser,” Joanna Batstone, the VP and lab director of the IBM Research center in Australia that published the study, told MobiHealthNews in an email.
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