Researchers Use AI for instant diagnosis of diabetic retinopathy

Researchers used artificial intelligence to encourage the instant identification of one of the main causes of blindness, diabetes mellitus related eye disease, in its first stages. Diabetic retinopathy is the leading cause of vision loss in adultsand its effect is growing worldwide, with 191 million people set to be impacted by 2030. There are no phase symptoms and the disease might already be advanced by the time people start losing their sight. Early identification and treatment could make a difference to how much vision a patient retains. Currently a group of Brazilian researchers have developed an image processing algorithm which may automatically discover one of the essential signs of disease, fluid on the retina, with a precision rate of 98 percent.

Dinesh Kant Kumar, Senior Researcher, RMIT, stated the procedure was economical and instantaneous. We know that only 50% of people with diabetes mellitus have routine eye examinations and one 3rd have never been checked, Kumar said. However the gold standard ways of diagnosing diabetic retinopathy is invasive or expensive, and frequently inaccessible in remote or developing portions of the world. Our AI driven strategy delivers results which are equally as accurate as clinical scans, but is based on retinal images that may be generated with regular optometry equipment. Making it faster and cheaper at discover this incurable disease might be life changing for the millions of individuals that are undiscovered and risk losing their sight.

Fluorescein angiography and optical coherence tomography scans are currently the most accurate clinical methods for diagnosing diabetic retinopathy. An alternative and cheaper method is analysing images of the retina that may be taken with comparatively cheap equipment called bottom chambers, but the process is manual, time intensive and reliable. To automate the evaluation of images, researchers in the Biosignals Laboratory in the School of Engineering at RMIT, together with collaborators in Brazil, utilized deep learning and artificial intelligence techniques. The plan they developed can spot the existence of fluid from damaged blood vessels, or exudates, inside the retina. The researchers believe that their strategy might possibly be used for widespread screening of at risk populations.

Undiagnosed diabetes mellitus is an enormous health issue here and around the globe, Kumar said. For every person in Australia who knows they’ve diabetes mellitus, another is currently living with diabetes mellitus, but isn’t diagnosed. In developing nations, the report is a diagnosed at four undiagnosed. This translates into millions of folks developing preventable and curable complications in diabetes related diseases. With further development, our technology has the potential to reduce that weight. The researchers are in discussion with producers of cameras about possible collaborations to advance the technology.

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