Glaucoma Screening along with Referral Justification using Large Deep Learning Model

Abstract

The novel addition of artificial intelligence to the current diagnostic methods is the screening and management of glaucoma. The main diagnostic method uses ophthalmoscopy to assess the optic nerve and tonometry to detect intraocular pressure. Central visual field tests, on the other hand, provide a clear picture of glaucoma patients’ functional vision and can provide more detailed information on the disease’s progression. However, the early identification and prediction of glaucoma is being revolutionized by AI-driven technologies, particularly self-supervised learning (SSL) and Vision Transformer (ViT)-based models. In order to improve glaucoma detection and classification based on color fundus photography images, a specially created SSL framework called RetFound uses advanced self-supervised learning. It learns detailed retinal structures from large-scale unnamed datasets, which lessens the need for large, fully manually annotated datasets while also greatly increasing diagnostic accuracy. In order to accurately detect glaucomatous alterations including optic nerve cupping, neuroretinal rim thinning, and retinal nerve fiber layer abnormalities, the Vision Transformer model further improves the feature extraction capabilities. In order to distinguish between distinct phases and subtypes of glaucoma and aid in the diagnosis process, this research investigates binary classification, glaucoma vs non-glaucoma, and multi-class classification. This study explores the difficulties RetFound faces, AI-powered approaches, and the future of glaucoma treatment; it is setting a new benchmark for proactive patient care and precise diagnosis.