ARTIFICIAL INTELLIGENCE IN OPHTHALMOLOGY

1. Artificial Intelligence in Ophthalmology: Promises, Hazards and Challenges
2. Basics of Artificial Intelligence for Ophthalmologists
3. Overview of Artificial Intelligence Systems in Ophthalmology
4. Autonomous Artificial Intelligence Safety and Trust
5. Technical Aspects of Deep Learning in Ophthalmology
6. Selected Image Analysis Methods for Ophthalmology
7. Experimental Artificial Intelligence Systems in Ophthalmology: An Overview
8. Artificial Intelligence in Age-Related Macular Degeneration (AMD)
9. AI and Glaucoma
10. Artificial Intelligence in Retinopathy of Prematurity
11. Artificial Intelligence in Diabetic Retinopathy
12. Google and DeepMind: Deep Learning Systems in Ophthalmology
13. Singapore Eye Lesions Analyzer (SELENA): The Deep Learning System for Retinal Diseases
14. Automatic Retinal Imaging and Analysis: Age-Related Macular Degeneration (AMD) within Age-Related Eye Disease Studies (AREDS)
15. Artificial Intelligence for Keratoconus Detection and Refractive Surgery Screening
16. Artificial Intelligence for Cataract Management
17. Artificial Intelligence in Refractive Surgery
18. Artificial Intelligence in Cataract Surgery Training
19. Artificial Intelligence in Ophthalmology Triaging
20. Deep Learning Applications in Ocular Oncology
21. Artificial Intelligence in Neuro-ophthalmology
22. Artificial Intelligence Using the Eye as a Biomarker of Systemic Risk
23. Artificial Intelligence in Calculating the IOL Power
24. Practical Considerations for AI Implementation in IOL Calculation Formulas

This book provides a wide-ranging overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms in ophthalmology. Expertly written chapters examine AI in age-related macular degeneration, glaucoma, retinopathy of prematurity and diabetic retinopathy screening. AI perspectives, systems and limitations are all carefully assessed throughout the book as well as the technical aspects of DL systems for retinal diseases including the application of Google DeepMind, the Singapore algorithm, and the Johns Hopkins algorithm.
Artificial Intelligence in Ophthalmology meets the need for a resource that reviews the benefits and pitfalls of AI, ML and DL in ophthalmology. Ophthalmologists, optometrists, eye-care workers, neurologists, cardiologists, internal medicine specialists, AI engineers and IT specialists with an interest in how AI can help with early diagnosis and monitoring treatment in ophthalmic patients will find this book to be an indispensable guide to an evolving area of healthcare technology.

Features
• Reviews current and predicted trends
• Written by world leading experts in the field
• Assesses how AI can be applied in ophthalmic healthcare