Diagnosis and Analysis of Glaucoma using AI and ML for Medical Imaging
- 1st Edition - July 1, 2026
- Latest edition
- Editor: Mohammad Sufian Badar
- Language: English
Diagnosis and Analysis of Glaucoma using AI and ML for Medical Imaging highlights the importance of early detection while also discussing and updating on current treatment option… Read more
This targeted resource is aimed at increasing understanding of this often asymptomatic, progressive eye disease, particularly in developing countries. Healthcare professionals, students, and policymakers will find this resource valuable with its straightforward, easy to understand, curriculum-aligned content. Its emphasis on practical applications and awareness-building make 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
- 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
Mohammad Sufian Badar
2.Anatomy and Physiology of Eye
Mohammad Sufian Badar
3.Impact of Current Glaucoma Medication of Ocular Surface
Mohammad Sufian Badar
4. Applications of AI Techniques in Healthcare and Disease Management
Mohammad Sufian Badar
5. Monitoring Glaucoma Progression
Mohammad Sufian Badar
6. Challenges of Glaucoma Diagnosis for Developing Countries
Mohammad Sufian Badar
7. Integrating ML and Optimization Techniques for Improved Disease Prediction
Mohammad Sufian Badar
8. Diagnosis and Care of Glaucoma Patients in Developing Countries
Mohammad Sufian Badar
9. Genetics, Types, and Risk Factors Associated with Glaucoma
Mohammad Sufian Badar
10. Glaucoma Imaging Techniques and Its Classification Using AI
Mohammad Sufian Badar
11. The impact of AI and ML in Glaucoma Diagnosis and Management
Mohammad Sufian Badar
12. Accuracy of Glaucoma Diagnosis by AI and ML in Screening and Clinical
Practice
Budheswar Dehury
13. Necessity of Making Glaucoma Diagnosis More Accessible in Developing Countries
Mohammad “Sufian” Badar
14. Future Prospects: Necessity of Making Glaucoma Diagnosis More Accessible in Developing Countries
Vojč Kocman
- Edition: 1
- Latest edition
- Published: July 1, 2026
- Language: English
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Mohammad Sufian Badar
Mohammad “Sufian” Badar, PhD is currently working as an Professor (Assistant) in the Department of Computer Science and Engineering, School of Engineering Sciences and Technology (SEST), Jamia Hamdard, New Delhi, India. Prior to that, he served as a Senior Teaching Faculty in the Department of Bioengineering at the University of California, Riverside, CA, USA. He served as an Analytics Architect in CenturyLink for more than a year in Denver, CO, USA. He possesses an excellent academic record with an MS degree in Molecular Science and Nanotechnology and a Ph.D. in Engineering from Louisiana Tech University Ruston, LA, USA, respectively. Before joining the Ph.D. program at Louisiana Tech University, he graduated with an MSc in Bioinformatics from Jamia Millia Islamia University, New Delhi, India. Dr. Sufian has over 18 years of teaching, research, and industry experience. He has published his research in conferences and highly reputed international journals. He has authored many chapters in the areas of Artificial Intelligence/Machine Learning and Blockchain/IoT. He has published six books with Elsevier, Springer Nature and Bentham, respectively. He recently submitted three book proposals with different publishers.
He is currently in the process of developing a device that, using Biosensors, can correlate the physiology of the human body with the emotion recognition algorithm, giving us a clear measure of the amount of stress hormones in the body. Currently, he and his group have developed an ML model that predicts COVID-19 infection based on symptoms only, and we are now working on increasing the accuracy of our model.