They are blind, and they don’t know it. Their world has always been hazy. They have never seen it any other way. Thousands of others are silently turning blind with diabetes, and don’t even know it. The challenge to help these people increases manifold when they are in remote areas, where no specialized treatment can reach them.
It is in cases like these, when a mere selfie can alert the doctors of impending visual problems, that Dr. Kaushik Murali, an ophthalmologist/ eye surgeon at Sankara Eye Hospital Bangalore, is hoping artificial intelligence can be the answer. The problem, if nipped in the bud, can save the vision of the child or a diabetic individual.
Murali and his team have annotated 8,000 retinal scan pictures, including a range of people with vision problems resulting from diabetes. This will help identify the beginnings of this specific cause of blindness, a disease called diabetic retinopathy. ROP is caused by type-two diabetes. They are training the algorithm to identify key markers of diabetic retinopathy, such as nerve tissue damage, swelling, and hemorrhaging. The disease creates lesions in the back of the retina that can lead to total blindness. “Today the algorithm has 98 percent capability of identifying any patient who has a ROP change,” said Murali.
Seventy million people have diabetes in India and 18 percent of diabetic Indians already have the ailment, according to the International Diabetes Federation. By 2045, India is projected to have 134 million cases, making India the country with the most number of diabetics,
Many diabetic patients assume that early signs of the disease are simply minor vision problems. Some don’t even know they are diabetic. In these cases, where blindness often is preventable if diabetic retinopathy is caught early, loss of vision is unnecessary. Medications, therapies, exercise, and a healthy diet are highly effective treatments for preventing further damage if the disease is diagnosed early enough,
The system is trained by deep learning. The program’s diagnosis for each image is compared with that of the ophthalmological panel and the parameters of the function are adjusted to reduce the error margin for each image. This process is repeated for each image until the program can make an accurate diagnosis based on the intensity of pixels in the retinal image. The results are extremely encouraging. The algorithm showed similar — in fact slightly better — levels of sensitivity and specificity as a panel of ophthalmologists.
A similar campaign is spearheading the cause of preventing blindness in children. A lazy eye will be quickly caught by the machine and preemptive action planned.
Ritu Marwah is an award winning author, chef, debate coach, and mother of two boys. She lives in the bay area and has deep experience in Silicon Valley start-ups as well as large corporations as a senior executive.