Digital Ophthalmological Image–based Eye Disease Prediction Model

This article addresses the problem of developing an intelligent model designed to predict eye diseases based on digital ophthalmic images. The main goal of the study is to create a forecasting model that, using deep learning algorithms, enables early detection of ophthalmic pathologies through retinal images and prediction of their likelihood of progression. During the research process, fundus images underwent an initial preprocessing stage, including noise reduction, contrast enhancement, and extraction of key anatomical structures. At the next stage, a classification and forecasting module based on convolutional neural networks was developed. Model performance was evaluated using accuracy, sensitivity (recall), the F1 score, and the ROC curve. The obtained results showed that the proposed approach enables faster and more stable detection of eye diseases compared to traditional diagnostic methods. It was also found that the model can support clinical decision-making by preliminarily assessing probable scenarios of disease progression. The practical significance of the study is explained by the potential to improve the quality of ophthalmological services through the introduction of digital diagnostic systems, to define early preventive measures, and to reduce physician workload. The proposed forecasting model is also considered a promising solution for telemedicine and remote screening systems.