A WEB-BASED AI SYSTEM FOR EYE DISEASE AND SEVERITY PREDICTION USING OCT IMAGE
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This study uses pictures called optical coherence tomography images along with information about the patient to find out if they have retinal diseases and how bad they are. The doctors looked at seven kinds of problems: choroidal neovascularization, drusen, DME, normal retina, RVO, ERM and vitreomacular interface disorder. They made a computer program that can tell which kind of problem a patient has. To make the program better at predicting how bad the disease is the doctors included information about the patient like how old they are, if they have diabetes, if they have high blood pressure and if they smoke. The program looks at the optical coherence tomography images. Finds the important things about them instead of using a more complicated method.The doctors used a lot of optical coherence tomography images, 84,568 to teach the program. It was very good at telling which kind of retinal problem a patient had. It was 97 percent of the time and it found the real problems 95 percent of the time and it did not say someone had a problem when they did not 94 percent of the time.The doctors did a lot of work to check how good the program was and they found out that it is very helpful for eye doctors to find retinal diseases early and to take care of patients. The program can find kinds of retinal diseases and tell how bad they are, which is very useful, for doctors.
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S, K. S. (2026). A Web-Based AI System for Eye Disease and Severity Prediction Using Oct Image. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.151
S, Kavya. "A Web-Based AI System for Eye Disease and Severity Prediction Using Oct Image." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.151.
S, Kavya. "A Web-Based AI System for Eye Disease and Severity Prediction Using Oct Image." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.151.
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