Artificial intelligence framework for multi-pathology risk assessment from retinal fundus images: deep learning approach to 15-disease screening

Frontiers in Medicine. 2026. Vol. 13. Article 1778404.

Vasilev R., Savchenko A., Blinov P., Svetina T., Kudin S., Romanenko N., Sarana Y., Khizhnyak G., Demchinsky A., Shcheglova T.

Automated disease screening systems face challenges when applied to multi-class medical image analysis, particularly under severe class imbalance inherent in clinical datasets. Retinal fundus imaging enables non-invasive screening for multiple ocular and systemic diseases simultaneously, yet current automated systems typically assess risk for only a single pathology or a limited disease range. We developed a comprehensive AI framework for simultaneous risk stratification of 15 distinct pathological conditions from retinal fundus images and report its preliminary evaluation in a real-world clinical setting.

Keywords: компьютерная диагностика, image analysis, applications to medicine, глубокое обучение, class imbalance, дисбаланс классов, fundus, глазное дно, анализ медицинских изображений, deep learning, computer-aided diagnosis