Artificial Intelligence in Glaucoma

This comprehensive examination represents the definitive resource on artificial intelligence applications in glaucoma prediction and management. The book synthesizes cutting-edge research demonstrating how convolutional neural networks, transformer architectures, and machine learning algorithms are revolutionizing glaucoma care through unprecedented diagnostic accuracy and predictive capabilities.

The work covers three critical domains: predicting glaucoma development in at-risk populations, monitoring disease progression, and assessing surgical intervention risks. With AI models achieving AUC values of 0.936 and consistently outperforming human experts in clinical prediction tasks, this book provides evidence-based insights into the transformative potential of artificial intelligence in ophthalmology.

Key features include detailed analysis of GLIM-Net architectures, LSTM networks for temporal modeling, and gated transformer networks achieving 96% accuracy in rapid progression detection. The book addresses practical implementation challenges, regulatory frameworks, ethical considerations, and cost-effectiveness analysis essential for successful clinical deployment.

From foundational AI concepts to advanced applications in personalized medicine, this work serves as an invaluable resource for ophthalmologists, healthcare administrators, AI researchers, and medical students seeking to understand the future of glaucoma care in the digital age.