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 options to slow disease progression, emphasizing the role of AI and ML in improving diagnosis and management. Sections explore causes, symptoms, diagnostic challenges, and treatment strategies for glaucoma, integrating insights on how artificial intelligence and machine learning models can enhance healthcare delivery. Practical case studies and discussions on how accessible AI tools can be utilized by healthcare workers, NGOs, students, and researchers to address diagnostic barriers prevalent in resource-limited settings are included.
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.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. ii 4. Dedication - p. v 5. Contents - p. vii 6. Contributors - p. xv 7. Preface - p. xxiii 8. Acknowledgment - p. xxv 9. Chapter 1 Epidemiology of glaucoma and role of glaucoma screening in diagnosis - p. 1 10. Chapter 2 Anatomy and physiology of eye - p. 23 11. Chapter 3 Impact of current glaucoma medication of ocular surface - p. 57 12. Chapter 4 Applications of artificial intelligence techniques in healthcare and disease treatment - p. 79 13. Chapter 5 Monitoring glaucoma progression - p. 97 14. Chapter 6 Challenges of glaucoma diagnosis for developing countries - p. 117 15. Chapter 7 Integrating multiple machine learning and optimization techniques for superior disease prediction - p. 137 16. Chapter 8 Diagnosis and care of glaucoma patients in developing countries - p. 173 17. Chapter 9 Genetics, types, and risk factors associated with glaucoma - p. 199 18. Chapter 10 Glaucoma imaging techniques and its classification using artificial intelligence - p. 225 19. Chapter 11 The impact of artificial intelligence and machine learning in the diagnosis and management of glaucoma - p. 251 20. Chapter 12 Accuracy of glaucoma diagnosis by artificial intelligence and machine learning in screening and clinical practice - p. 267 21. Chapter 13 Necessity of making glaucoma diagnosis more accessible in developing countries - p. 293 22. Chapter 14 Future prospects necessity of making glaucoma diagnosis more accessible in developing countries - p. 303 23. Index - p. 321
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 options to slow disease progression, emphasizing the role of AI and ML in improving diagnosis and management. Sections explore causes, symptoms, diagnostic challenges, and treatment strategies for glaucoma, integrating insights on how artificial intelligence and machine learning models can enhance healthcare delivery. Practical case studies and discussions on how accessible AI tools can be utilized by healthcare workers, NGOs, students, and researchers to address diagnostic barriers prevalent in resource-limited settings are included.
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.
1. Cover image - p. c 2. Title page - p. i 3. Copyright - p. ii 4. Dedication - p. v 5. Contents - p. vii 6. Contributors - p. xv 7. Preface - p. xxiii 8. Acknowledgment - p. xxv 9. Chapter 1 Epidemiology of glaucoma and role of glaucoma screening in diagnosis - p. 1 10. Chapter 2 Anatomy and physiology of eye - p. 23 11. Chapter 3 Impact of current glaucoma medication of ocular surface - p. 57 12. Chapter 4 Applications of artificial intelligence techniques in healthcare and disease treatment - p. 79 13. Chapter 5 Monitoring glaucoma progression - p. 97 14. Chapter 6 Challenges of glaucoma diagnosis for developing countries - p. 117 15. Chapter 7 Integrating multiple machine learning and optimization techniques for superior disease prediction - p. 137 16. Chapter 8 Diagnosis and care of glaucoma patients in developing countries - p. 173 17. Chapter 9 Genetics, types, and risk factors associated with glaucoma - p. 199 18. Chapter 10 Glaucoma imaging techniques and its classification using artificial intelligence - p. 225 19. Chapter 11 The impact of artificial intelligence and machine learning in the diagnosis and management of glaucoma - p. 251 20. Chapter 12 Accuracy of glaucoma diagnosis by artificial intelligence and machine learning in screening and clinical practice - p. 267 21. Chapter 13 Necessity of making glaucoma diagnosis more accessible in developing countries - p. 293 22. Chapter 14 Future prospects necessity of making glaucoma diagnosis more accessible in developing countries - p. 303 23. Index - p. 321
