In their study published online in Ophthalmology - the journal of the American Academy of Ophthalmology - the researchers describe the use of deep-learning methods to create an automated algorithm that can detect diabetic retinopathy (DR), a condition that damages the blood vessels at the back of the eye, potentially causing blindness.
The research shows that an artificial intelligence-based grading algorithm can be used to identify, with high reliability, which patients should be referred to an ophthalmologist for further evaluation and treatment, Theodore Leng, M.D., lead author, explains. The algorithm does not require any specialized, inaccessible, or costly computer equipment to grade images. It can be run on a common personal computer or smartphone with average processors.
Leng: “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. We hope that this technology will have the greatest impact in parts of the world where ophthalmologists are in short supply."
The algorithm could identify all disease stages, from mild to severe, with an accuracy rate of 94 percent. It would be these patients that should see an ophthalmologist for further examination. An ophthalmologist is a physician who specializes in the medical and surgical treatment of eye diseases and conditions.
Ophthalmologists typically diagnose the presence and severity of diabetic retinopathy by direct examination of the back of the eye and by evaluation of color photographs of the fundus, the interior lining of the eye. Considering the large number of diabetes patients globally, this is an expensive and time-consuming process. Also, previous studies have shown that detection is somewhat subjective, even among trained specialists. This is why an effective, automated algorithm could potentially reduce the rate of worldwide blindness.
Approval from the U.S. Food and Drug Administration is required before the algorithm can be used in patients on a broad basis. Dr. Leng and his team expect to conduct pilot trials in the near future.
The research shows that an artificial intelligence-based grading algorithm can be used to identify, with high reliability, which patients should be referred to an ophthalmologist for further evaluation and treatment, Theodore Leng, M.D., lead author, explains. The algorithm does not require any specialized, inaccessible, or costly computer equipment to grade images. It can be run on a common personal computer or smartphone with average processors.
Leng: “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. We hope that this technology will have the greatest impact in parts of the world where ophthalmologists are in short supply."
Deep learning on the rise
Deep learning is on the rise in computer science and medicine because it can teach computers to do what our brains do naturally. Dr. Leng and his colleagues created the algorithm using over 75,000 images from a wide range of patients representing several ethnicities, and then used it to teach a computer to identify between healthy patients and those with any stage of disease, from mild to severe.The algorithm could identify all disease stages, from mild to severe, with an accuracy rate of 94 percent. It would be these patients that should see an ophthalmologist for further examination. An ophthalmologist is a physician who specializes in the medical and surgical treatment of eye diseases and conditions.
Low awareness
Diabetes affects more than 415 million people worldwide. About 45 percent of diabetic patients are likely to have diabetic retinopathy at some point in their life. But fewer than half of patients are aware of their condition. Early detection and treatment are integral to combating this worldwide epidemic of preventable vision loss.Ophthalmologists typically diagnose the presence and severity of diabetic retinopathy by direct examination of the back of the eye and by evaluation of color photographs of the fundus, the interior lining of the eye. Considering the large number of diabetes patients globally, this is an expensive and time-consuming process. Also, previous studies have shown that detection is somewhat subjective, even among trained specialists. This is why an effective, automated algorithm could potentially reduce the rate of worldwide blindness.
Approval from the U.S. Food and Drug Administration is required before the algorithm can be used in patients on a broad basis. Dr. Leng and his team expect to conduct pilot trials in the near future.
