Diagnosis and Analysis of Glaucoma using AI and ML for Medical Imaging
- 1st Edition - April 1, 2026
- Editor: Mohammad Sufian Badar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 4 4 9 1 1 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 4 9 1 2 - 3
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 diseas… Read more
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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.
- Demonstrates how AI and ML are applied in glaucoma diagnosis and management, providing practical insights for students and researchers in medical informatics and healthcare
- Employs clear, precise language that makes complex concepts accessible to readers without a computer science background, facilitating interdisciplinary understanding and collaboration
- Provides detailed case studies and implementation guidelines, enabling researchers and practitioners to translate theoretical AI techniques into real-world diagnostic tools
AI researchers, data scientists, and machine learning practitioners interested in applying their skills to healthcare challenges, particularly in medical imaging and ophthalmology
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
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
- Edition: 1
- Published: April 1, 2026
- Language: English
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Mohammad Sufian Badar
Dr. M.S Badar, MS, PhD served as a Teaching Faculty in the Department of Bioengineering at the University of California, Riverside, CA, USA. He graduated with an MS degree in Molecular Science and Nanotechnology and Ph.D. in Engineering from Louisiana Tech University Ruston, LA, USA, respectively. Dr. Badar has over 14 years of teaching, research, and industry experience. He has authored a chapter in a book about Machine Learning and Molecular Modeling. He has developed an algorithm for Face Detection, Recognition, and Emotion Recognition. He has developed a device that, by using a Biosensor, can correlate the physiology of the human body with the emotion recognition algorithm, giving us an accurate measurement of stress hormones in the body. His group is developing an ML Model which predicts Covid infection based on the severity of symptoms (mild, moderate, and severe) and if they have had contact with a Covid patient.
Affiliations and expertise
Senior Teaching Faculty, Department of Bioengineering, University of California, Riverside, CA, USA