The retinal damage in diabetes patients that can potentially cause permanent blindness. Depending on the details of the case and the skill of the physician, it can take them anywhere from 10 to 40 minutes to examine the blood vessel network in the retina image and make a diagnosis. Presented in Pattern Recognition Letters, the team’s AI solution delivers the result instantaneously, leaving it to the eye doctor to review and confirm the findings.

Eye care professionals use specialized cameras to take retina images and study them, manually segmenting the photo. This involves differentiating between the background and blood vessels of varying length, width, and tortuosity — the latter refers to a swelling pattern. Features of the retinal blood vessel network can point to diabetic retinopathy, as well as other eye and cardiovascular diseases — even atherosclerosis. However, manual image segmentation is very difficult, time-consuming, and error-prone.

Now, researchers have automated this daunting task in a way that promises not just to save time to the eye doctors but possibly to eliminate some human errors. By training their AI system on a highly reliable sample of double-checked cases studied by top physicians, the team has achieved exceptionally good performance in tests on three state-of-the-art datasets. That includes an accuracy of over 97% and a sensitivity of over 84% on the industry-standard database called DRIVE.

In this study, we propose a neural network architecture different from those used by prior approaches, which tend to overlook the microvessels found in the retina, Getahun said. We also introduced an algorithm for tuning the output of the model by understanding the underlying nature of the retina vessel image data. This helps avoid the misclassification of vessel pixels as background.

Source: https://new.skoltech.ru/en/news/ai-maps-blood-vessels-eye-retina-better-diagnostics