Diagnosis and Analysis of Glaucoma using AI and ML for Medical Imaging is a targeted resource aimed at increasing understanding of this often asymptomatic, progressive eye disease, particularly in developing countries. It highlights the importance of early detection and discusses current treatment options to slow disease progression, emphasizing the role of AI and ML in improving diagnosis and management. The book explores the causes, symptoms, diagnostic challenges, and treatment strategies for glaucoma, integrating insights on how artificial intelligence and machine learning models can enhance healthcare delivery. It includes practical case studies and discusses how accessible AI tools can be utilized by healthcare workers, NGOs, students, and researchers to address diagnostic barriers prevalent in resource-limited settings. This publication benefits a broad audience, including healthcare professionals, students, and policymakers, by providing curriculum-aligned content that is straightforward and easy to understand. Its emphasis on practical applications and awareness-building makes it a valuable tool for advancing glaucoma care and fostering interdisciplinary collaboration in eye health.
1. Introduction to Glaucoma: Epidemiology and the Transformative Role of
AI in Screening and Early Diagnosis
2. Anatomy and Physiology of the Eye: Enhancing Understanding Through
AI-Driven Modeling
3. Evaluating the Impact of Current Glaucoma Medications on Ocular Surface:
AI-Assisted Monitoring and Optimization
4. AI-Enabled Monitoring of Glaucoma Progression: Innovations in Tracking
Disease Dynamics
5. AI-Driven Diagnosis and Care Strategies for Glaucoma Patients in
Developing Countries
6. Genetics, Types, and Risk Factors of Glaucoma: Insights Gained Through AI
and Machine Learning
7. Advanced Glaucoma Imaging Techniques: Classification and Analysis Using AI
Algorithms
8. The Role of AI and Machine Learning in Revolutionizing Glaucoma Diagnosis
and Management
9. Assessing the Accuracy of AI and ML in Glaucoma Screening and Clinical
Practice
10. Future Perspectives: Leveraging AI to Make Glaucoma Diagnosis More
Accessible and Effective in Developing Countries
Mohammad Sufian Badar, PhD, is an Assistant Professor in the Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi. Previously, he was a Senior Teaching Faculty at UC Riverside, CA, USA, and an Analytics Architect at CenturyLink in Denver, CO, USA. He holds an MS in Molecular Science and Nanotechnology and a PhD in Engineering from Louisiana Tech University. He earned an MSc in Bioinformatics from Jamia Millia Islamia, New Delhi. With many years of teaching, research, and industry experience, Dr. Badar has published in conferences and journals, authored chapters on AI, machine learning, blockchain, IoT, and computational biology and has edited and authored several books in his area of interest like AI, ML, computational biology, and the integration of AI/ ML with health sciences.
