Chennai: Diabetic retinopathy is a medical condition in which damage occurs to the retina due to diabetes and is a leading cause of blindness. Imaging and lab testing is essential for the diagnosis of diabetic retinopathy and 90 percent of the vision loss due to diabetic retinopathy is preventable by early detection and timely treatment.
In a recent study published in the international ‘Eye’ journal, by Dr R. Rajalakshmi, a senior ophthalmologist at the Madras Diabetes Research Foundation, it was revealed that use of automated artificial intelligence along with smartphone-based retinal imaging can be used to detect diabetic retinopathy.
The recent study highlights the role and accuracy of automated artificial intelligence based diabetes research analysis in a smartphone-based retinal imaging to detect retinopathy.
The retinal photographs were taken for people with diabetes using Remidio Fundus on the phone, a smartphone based imaging device that has sensitivity up to 95.8 percent for detecting retinopathy and 99.1 percent for sight-threatening diabetic retinopathy.
The retinal photos were graded by the retina specialists as well as by the automated AI EyeArt software for detecting and classifying retinopathy and an analysis of the comparative predictions was done.
Until recently, diabetic retinopathy was detected only by ophthalmologists and retina specialists who examine the back of the eye (retina) or by retinal colour photography taken using expensive retinal cameras, which had to be later interpreted by the trained eye doctors.
However, AI can now be used to grade retinal images of people with diabetes and determine which patients have retinopathy and the ones with sight-threatening retinopathy, needing urgent referral to the ophthalmologist.
Senior ophthalmologist Dr V Mohan of Dr Mohan's Diabetes Specialities Centre said that one out of five Indians with diabetes are at risk for developing diabetic retinopathy. With an increase in the incidence of diabetes, the computer-based analysis-using artificial intelligence of the retinal photographs using smartphone-based cameras would reduce the burden of the health systems in screening for sight threatening retinopathy, he said.
