Short description:

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, 2nd edition has been fully revised with the latest advancements in the field. New chapters highlight topics such as the threats to the validity of the predictive system performance for ophthalmology, challenges in the deployment of AI for diabetic retinopathy screening and the use of AI in calculating the IOL power to name just a few. This book 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.

Long description:

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, 2nd edition has been fully revised with the latest advancements in the field. New chapters highlight topics such as the threats to the validity of the predictive system performance for ophthalmology, challenges in the deployment of AI for diabetic retinopathy screening and the use of AI in calculating the IOL power to name just a few. This book 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.

Table of Contents:

1. Artificial intelligence in Medicine.- 2. Artificial intelligence in AMD.- 3. Artificial intelligence in Glaucoma.- 4. Artificial intelligence in Retinopathy of Prematurity.- 5. Artificial intelligence in Diabetic Retinopathy Screening.- 6. The overview of available artificial intelligence systems in Ophthalmology.- 7 The overview of experimental artificial intelligence systems in Ophthalmology.- 8. Technical aspects of deep learning in Ophthalmology.- 9. Deep learning systems for retinal diseases.- 10. Google DeepMind.- 11. Singapore algorithm.- 12. Johns Hopkins algorithm.- 13. Perspectives of artificial intelligence in Ophthalmology.- 14. Limitations of artificial intelligence in Ophthalmology.- 15. Artificial intelligence in calculating the IOL power.