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Artificial intelligence in glaucoma

BACKGROUND: Artificial Intelligence (AI) is an area of computer science that encompasses the creation of intelligent machines that work and react like humans. It deals with the development algorithms that seek to simulate human brain and also mimic cognitive functions typically associated with the h...

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Autores principales: Nair, Megha, Tagare, Shivraj, Venkatesh, Rengaraj, Odayappan, Annamalai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332988/
https://www.ncbi.nlm.nih.gov/pubmed/35502116
http://dx.doi.org/10.4103/ijo.IJO_1015_22
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author Nair, Megha
Tagare, Shivraj
Venkatesh, Rengaraj
Odayappan, Annamalai
author_facet Nair, Megha
Tagare, Shivraj
Venkatesh, Rengaraj
Odayappan, Annamalai
author_sort Nair, Megha
collection PubMed
description BACKGROUND: Artificial Intelligence (AI) is an area of computer science that encompasses the creation of intelligent machines that work and react like humans. It deals with the development algorithms that seek to simulate human brain and also mimic cognitive functions typically associated with the human mind such as learning and problem solving. PURPOSE: Do we need artificial intelligence in Glaucoma? Glaucoma is the second most common cause of blindness in the world. Its prevalence was over 60 million in 2010 and over 80 million by 2020. It is so common, yet so easily overlooked. More importantly, about 50% of patients in developed countries and 90% in developing countries are unaware of having glaucoma. Early detection can delay the progression of glaucoma. Hence the time is ripe to advovate glaucoma screening. SYNOPSIS: The application of AI in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract etc AI involves mainly 1. machine learning that are algorithms with the ability to learn without being explicitly programmed and 2. deep learning in which artificial neural networks adapt and learn from vast amounts of data. But there are limitations to screening - such as disparity between ophthalmologist:patient ratio and also the availability of the specialty services. The large amount of data acquired from patients makes it nearly impossible for ophthalmologists to screen them with equal efficacy and consistency. HIGHLIGHTS: AI in glaucoma aims at including factors such as clinical data, genomic data, life style behaviors, risk factors, and medical history to predict the risk of developing glaucoma, help customise the most appropriate management protocol for a given patient, and estimate prognosis and surgical success. VIDEO LINK: https://youtu.be/IwYS7wDMhkY
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spelling pubmed-93329882022-07-29 Artificial intelligence in glaucoma Nair, Megha Tagare, Shivraj Venkatesh, Rengaraj Odayappan, Annamalai Indian J Ophthalmol IJO Videos BACKGROUND: Artificial Intelligence (AI) is an area of computer science that encompasses the creation of intelligent machines that work and react like humans. It deals with the development algorithms that seek to simulate human brain and also mimic cognitive functions typically associated with the human mind such as learning and problem solving. PURPOSE: Do we need artificial intelligence in Glaucoma? Glaucoma is the second most common cause of blindness in the world. Its prevalence was over 60 million in 2010 and over 80 million by 2020. It is so common, yet so easily overlooked. More importantly, about 50% of patients in developed countries and 90% in developing countries are unaware of having glaucoma. Early detection can delay the progression of glaucoma. Hence the time is ripe to advovate glaucoma screening. SYNOPSIS: The application of AI in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract etc AI involves mainly 1. machine learning that are algorithms with the ability to learn without being explicitly programmed and 2. deep learning in which artificial neural networks adapt and learn from vast amounts of data. But there are limitations to screening - such as disparity between ophthalmologist:patient ratio and also the availability of the specialty services. The large amount of data acquired from patients makes it nearly impossible for ophthalmologists to screen them with equal efficacy and consistency. HIGHLIGHTS: AI in glaucoma aims at including factors such as clinical data, genomic data, life style behaviors, risk factors, and medical history to predict the risk of developing glaucoma, help customise the most appropriate management protocol for a given patient, and estimate prognosis and surgical success. VIDEO LINK: https://youtu.be/IwYS7wDMhkY Wolters Kluwer - Medknow 2022-05 2022-04-28 /pmc/articles/PMC9332988/ /pubmed/35502116 http://dx.doi.org/10.4103/ijo.IJO_1015_22 Text en Copyright: © 2022 Indian Journal of Ophthalmology https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle IJO Videos
Nair, Megha
Tagare, Shivraj
Venkatesh, Rengaraj
Odayappan, Annamalai
Artificial intelligence in glaucoma
title Artificial intelligence in glaucoma
title_full Artificial intelligence in glaucoma
title_fullStr Artificial intelligence in glaucoma
title_full_unstemmed Artificial intelligence in glaucoma
title_short Artificial intelligence in glaucoma
title_sort artificial intelligence in glaucoma
topic IJO Videos
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9332988/
https://www.ncbi.nlm.nih.gov/pubmed/35502116
http://dx.doi.org/10.4103/ijo.IJO_1015_22
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