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Titlebook: Artificial Intelligence in Ophthalmology; Andrzej Grzybowski Book 2021 Springer Nature Switzerland AG 2021 Automatic software.Deep learnin

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發(fā)表于 2025-3-21 18:59:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Artificial Intelligence in Ophthalmology
影響因子2023Andrzej Grzybowski
視頻videohttp://file.papertrans.cn/163/162513/162513.mp4
發(fā)行地址Reviews current and predicted trends.Written by world leading experts in the field.Assesses how AI can be applied in ophthalmic healthcare
圖書(shū)封面Titlebook: Artificial Intelligence in Ophthalmology; Andrzej Grzybowski Book 2021 Springer Nature Switzerland AG 2021 Automatic software.Deep learnin
影響因子.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..
Pindex Book 2021
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https://doi.org/10.1007/978-3-642-38944-3l data, a prerequisite to translating the techniques to patient care, is often times lacking or unsuccessful. Despite this, the potential of AI as a diagnostic and screening tool to improve patient care highlights the continued need for experimental approaches to be applied to tasks which have the p
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發(fā)表于 2025-3-22 05:10:20 | 只看該作者
Nach dem Crew Resource Managementwith DL models. The ability of extracting meaningful representations from high dimensional and complex multi-modal data enables DL system to achieve high accuracy of glaucoma diagnosis and prognosis and to discover new knowledges to improve our current understanding of glaucoma. Though DL algorithms
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發(fā)表于 2025-3-22 09:51:51 | 只看該作者
Nach dem Crew Resource Managementmic risk factors, highlighting the importance of controlling these to prevent visual impairment. Compared to 17 human assessors, SELENA performed with comparable accuracy and spent significantly less time. SELENA also demonstrated the potential of using DL systems in developing countries. In Zambia
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Technical Aspects of Deep Learning in Ophthalmology,nal neural networks replace dense matrix multiplication in neural networks with convolution and pooling operations to resemble the biological process of animal visual cortex. Recurrent operations in recurrent neural networks allow models to store past history. Finally, we provide a brief introductio
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Experimental Artificial Intelligence Systems in Ophthalmology: An Overview,l data, a prerequisite to translating the techniques to patient care, is often times lacking or unsuccessful. Despite this, the potential of AI as a diagnostic and screening tool to improve patient care highlights the continued need for experimental approaches to be applied to tasks which have the p
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發(fā)表于 2025-3-23 01:39:28 | 只看該作者
AI and Glaucoma,with DL models. The ability of extracting meaningful representations from high dimensional and complex multi-modal data enables DL system to achieve high accuracy of glaucoma diagnosis and prognosis and to discover new knowledges to improve our current understanding of glaucoma. Though DL algorithms
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