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Artificial Intelligence in Ophthalmology Editors (view affiliations) / edited by Andrzej Grzybowski

Colaborador(es): Grzybowski, Andrzej, editor | SpringerLink (Online service).
Tipo de material: materialTypeLabelE-bookSeries (Medicine (Springer-11650)).Editor: Cham : Springer Nature Switzerland Imprint: Springer, 2021Edición: 1st ed. 2021.Descripción: 1 recurso en línea VI, 286 páginas) : 9 ilustraciones, 99 ilustraciones a color.ISBN: 9783030786014.Tema: Oftalmología | Medicina | Public healthRecursos en línea: Acceso a este recurso digital (usuarios Universidad Europea de Valencia)Digital Resources Resumen: 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.
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LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Valencia Digital Acceso Electrónico (UEV) Ciencias de la Salud RE46 .A78 2020 EB (Navegar estantería(Abre debajo)) Acceso electrónico
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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.

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