Overview

This authoritative work presents a comprehensive examination of artificial intelligence applications in glaucoma diagnosis, addressing one of the most critical challenges in modern ophthalmology. Author Walid Saleh delivers an in-depth analysis of explainable AI (XAI) techniques applied to medical imaging, with particular focus on retinal fundus photography for early glaucoma detection. The book systematically explores various neural network architectures including VGG, ResNet, and DenseNet, providing comparative analysis of their performance in clinical settings. A significant contribution is the introduction of the novel SCIM (SHAP-CAM Interpretable Mapping) approach, which combines the strengths of different explainability techniques to achieve superior clinical interpretability. Key features include rigorous clinical validation by experienced ophthalmologists, comprehensive performance benchmarking across 28,539 consolidated dataset images, detailed analysis of computational efficiency and technical requirements, and practical implementation strategies for healthcare systems. The work addresses crucial regulatory and ethical considerations while providing actionable guidance for clinical adoption. This essential resource serves multiple audiences: ophthalmologists seeking to understand AI-assisted diagnosis, AI researchers developing medical applications, healthcare administrators planning technology implementations, and regulatory professionals overseeing medical AI systems. With its evidence-based approach and practical focus, this book represents a definitive guide to the intersection of artificial intelligence and glaucoma care.

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