Diagnosis and Analysis of Glaucoma using AI and ML for Medical Imaging
- 1st Edition - April 1, 2026
- Latest edition
- Editor: Mohammad Sufian Badar
- Language: English
Diagnosis and Analysis of Glaucoma using AI and ML for Medical Imaging offers a comprehensive overview of glaucoma—a progressive, often symptomless eye disease that is especially c… Read more
Diagnosis and Analysis of Glaucoma using AI and ML for Medical Imaging offers a comprehensive overview of glaucoma—a progressive, often symptomless eye disease that is especially challenging in developing regions. The book underscores the critical importance of early detection and details how artificial intelligence and machine learning are transforming diagnosis and care. Readers will find in-depth discussions about the underlying causes, common symptoms, and the hurdles faced in identifying glaucoma. By weaving together clinical insights and technological advancements, the book demonstrates how new tools can slow disease progression and improve patient outcomes in diverse healthcare environments.
In addition to foundational medical knowledge, the publication features practical case studies and highlights accessible AI solutions for healthcare workers, NGOs, students, and researchers tackling diagnostic gaps in resource-limited settings. Its clear, curriculum-aligned content makes it relevant for professionals, students, and policymakers.
In addition to foundational medical knowledge, the publication features practical case studies and highlights accessible AI solutions for healthcare workers, NGOs, students, and researchers tackling diagnostic gaps in resource-limited settings. Its clear, curriculum-aligned content makes it relevant for professionals, students, and policymakers.
- Demonstrates how AI and ML are applied in glaucoma diagnosis and management
- Employs clear, precise language that makes complex concepts accessible to readers without a computer science background
- 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
- Latest edition
- 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