AI-Driven Predictive Analytics

Abstract

Early detection is essential for managing glaucoma, a leading cause of irreversible blindness that often progresses without noticeable symptoms until significant vision loss has occurred. This study examines the application of Convolutional Neural Networks (CNNs), an emerging deep learning technology, for automating and enhancing glaucoma diagnosis. By analysing retinal fundus and optical coherence tomography (OCT) images, CNNs effectively detect structural changes such as optic nerve cupping and retinal nerve fibre layer thinning with high accuracy. Compared to traditional diagnostic methods, CNNs offer advantages including enhanced sensitivity, specificity, automation, and scalability. This research underscores the potential of integrating Deep Learning based, CNN systems into clinical workflows, paving the way for improved glaucoma screening and management.