Automated Algorithm Helps Computers Detect Diabetic Retinopathy

An automated algorithm that detects diabetic retinopathy has been identified by researchers at the Byers Eye Institute at Stanford University.

Diabetic retinopathy, a potential cause of blindness, is a disease that damages small blood vessels in the eye’s retina. About one in 11 adults worldwide has diabetes, and about 45 percent of those individuals are likely to some day develop retinopathy.

The investigators showed than an artificial intelligence-based algorithm can reliably identify patients requiring in-depth eye evaluation and treatment. The algorithm can be run on smartphones, or personal computers and does not require any expensive equipment to view images.

“If properly implemented on a worldwide basis, this algorithm has the potential to reduce the workload on doctors and increase the efficiency of limited healthcare resources,” said lead study author Theodore Leng, M.D. “We hope that this technology will have the greatest impact in parts of the world where ophthalmologists are in short supply.”

An algorithm is a set of rules to follow when making calculations. Leng’s algorithm, based on more than 75,000 eye images from patients of various ethnic backgrounds, was used to teach a computer to discriminate between healthy eyes, and those with disease. The algorithm was subsequently able to identify all retinopathy stages, mild to severe, with 94 percent accuracy.

Currently, diabetic retinopathy is diagnosed through a direct eye exam, and by evaluating color photos of the eye’s interior lining, or fundus. Since this is time-consuming, expensive, and a somewhat subjective process, having an automated algorithm that detects retinopathy could help reduce the incidence of vision loss globally.

Source: American Academy of Ophthalmology / Newswise
Photo credit: jfcherry

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