Diabetic retinopathy is a severe eye condition that can cause blindness in individuals with diabetes. It develops when high levels of blood sugar damage blood vessels found in the retina. American guidelines encourage individuals with type II diabetes to be screened at diagnosis, then once every year if no issue comes up.
On the other hand, the NHS encourages diabetics to go for eye screening every 1 or 2 years. To screen for the condition, health professionals take pictures of the eye’s rear interior wall.
While screening can help prevent vision loss, not every patient can access these services, primarily due to their cost. Dr. Roomasa Channa, a retina specialist, believes that having easier access to the tests would help many patients.
Given that interpreting the pictures is somewhat taxing, some experts have proposed the use of artificial intelligence in identifying diabetic retinopathy. The use of AI could also make the process cheaper, with experts adding that it could decide if patients needed to see an eye specialist.
A system that can identify the condition was developed by Portugal-based Retmarker. This system has been designed to pick out pictures that may have issues and send them to an expert for additional examination.
The firm’s chief executive João Diogo Ramos revealed that the tool was currently being used as support, offering additional data to humans who’d then decide how to proceed. Ramos believes that fearing change is limiting the adoption of artificial-intelligence powered diagnostic tools like this.
Given that cost is a major hindrance to patients undergoing screening, Retmarker plans to offer its service at €5 ($5.2) per screening. It should be noted though that this cost may vary according to location and volume. Additionally, patients in America may pay more because medical billing codes are set substantially higher.
EyeArt, developed by Eyenuk, has also been cleared by the FDA for the autonomous detection of mild to vision-threatening diabetic retinopathy.
Independent research suggests that both systems have acceptable rates of specificity and sensitivity. Specificity focuses on how efficient the tool is at detecting the condition’s absence while sensitivity centers on how efficient the test is at detecting the condition.
While this is welcome news, these systems still need further development.
Poor-quality images can cause false positives, with significantly high sensitivity also being linked to false positives. Researchers from different firms are currently working to better their systems, bringing them closer to helping more diabetic individuals.
AI is making significant inroads into the medical field and other industries. With companies like Intel Corp. (NASDAQ: INTC) focused on developing a wide array of AI solutions, it is just a matter of time before the existing ways of performing tasks are replaced by these cutting-edge technologies.
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