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
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: 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.