Published Paper Details:

Addressing visual disorders--such as cataracts, retinal degeneration from diabetes, and elevated intraocular pressure--at their onset is key to avoiding irreversible sight damage in aging and high-risk populations. Deep learning, as an advanced subset of modern computational intelligence, has reshaped the landscape of automated medical diagnostics, particularly in ophthalmology. This report investigates its use in recognizing three prominent vision-related disorders--cataract, diabetic retinal complications, and glaucoma--by highlighting crucial factors such as algorithm design, data variability, and real-world clinical integration. Contemporary neural systems, including convolution-driven architectures and attention-based visual models, are employed to extract both structural and contextual details from retinal imagery like fundus scans, OCT outputs, and slit-lamp visuals. Despite their promise, these systems often struggle with the limited availability of high-quality, annotated data--commonly affected by class disparities or visual inconsistencies due to equipment differences. To enhance detection accuracy and generalization, practitioners utilize methods like domain-adapted transfer learning, synthetic augmentation, and precision-tuning based on ocular features. Furthermore, clinical implementation demands interpretable models, regulatory validation, and seamless integration with electronic health records. Real-world deployments in telemedicine platforms and mobile eye-care units have demonstrated the scalability and cost-effectiveness of AI-driven diagnostics, especially in resource-limited settings. By addressing both technical and clinical challenges, deep learning offers a promising pathway toward timely and accurate detection of vision-threatening conditions.