The utilization of artificial intelligence in glaucoma: diagnosis versus screening.

With advancements in the implementation of artificial intelligence (AI) in different ophthalmology disciplines, it continues to have a significant impact on glaucoma diagnosis and screening. This article explores the distinct roles of AI in specialized ophthalmology clinics and general practice, highlighting the critical balance between sensitivity and specificity in diagnostic and screening models. Screening models prioritize sensitivity to detect potential glaucoma cases efficiently, while diagnostic models emphasize specificity to confirm disease with high accuracy. AI applications, primarily using machine learning (ML) and deep learning (DL), have been successful in detecting glaucomatous optic neuropathy from colored fundus photographs and other retinal imaging modalities. Diagnostic models integrate data extracted from various forms of modalities (including tests that assess structural optic nerve damage as well as those evaluating functional damage) to provide a more nuanced, accurate and thorough approach to diagnosing glaucoma. As AI continues to evolve, the collaboration between technology and clinical expertise should focus more on improving specificity of glaucoma diagnostic models to assess ophthalmologists to revolutionize glaucoma diagnosis and improve patients care.

Using soft computing techniques to diagnose Glaucoma disease.

Glaucoma is a major cause of blindness. Most patients start to observe that late after the disease causes a high level of damage in the optic nerve head and the high percentage of vision loss. Early diagnosis and treatment are essential and must be taken. Image processing mass-screening and machine learning classification can support early and automatic diagnosis of Glaucoma symptoms so as to take protective measures and to extend symptom-free life of patients. This paper proposes improved techniques to extract disease-related and image-based features. Support Vector Machines and Genetically-Optimized Artificial Neural Networks, pronounced machine learning algorithms, are fine-tuned to combine the two set of features in one automated image classification system. The proposed methodology was applied to a dataset of 106 retina images obtained from three hospitals. The proposed system automatically detected Glaucoma using Support Vector Machines technique with 100% specificity and 87% accuracy. Artificial Neural Network classified the images with 98% accuracy.