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Artificial intelligence for diabetic retinopathy
Diabetic retinopathy (DR) is an important cause of blindness globally, and its prevalence is increasing. Early detection and intervention can help change the outcomes of the disease. The rapid development of artificial intelligence (AI) in recent years has led to new possibilities for the screening...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812665/ https://www.ncbi.nlm.nih.gov/pubmed/34995039 http://dx.doi.org/10.1097/CM9.0000000000001816 |
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author | Li, Sicong Zhao, Ruiwei Zou, Haidong |
author_facet | Li, Sicong Zhao, Ruiwei Zou, Haidong |
author_sort | Li, Sicong |
collection | PubMed |
description | Diabetic retinopathy (DR) is an important cause of blindness globally, and its prevalence is increasing. Early detection and intervention can help change the outcomes of the disease. The rapid development of artificial intelligence (AI) in recent years has led to new possibilities for the screening and diagnosis of DR. An AI-based diagnostic system for the detection of DR has significant advantages, such as high efficiency, high accuracy, and lower demand for human resources. At the same time, there are shortcomings, such as the lack of standards for development and evaluation and the limited scope of application. This article demonstrates the current applications of AI in the field of DR, existing problems, and possible future development directions. |
format | Online Article Text |
id | pubmed-8812665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-88126652022-02-18 Artificial intelligence for diabetic retinopathy Li, Sicong Zhao, Ruiwei Zou, Haidong Chin Med J (Engl) Review Articles Diabetic retinopathy (DR) is an important cause of blindness globally, and its prevalence is increasing. Early detection and intervention can help change the outcomes of the disease. The rapid development of artificial intelligence (AI) in recent years has led to new possibilities for the screening and diagnosis of DR. An AI-based diagnostic system for the detection of DR has significant advantages, such as high efficiency, high accuracy, and lower demand for human resources. At the same time, there are shortcomings, such as the lack of standards for development and evaluation and the limited scope of application. This article demonstrates the current applications of AI in the field of DR, existing problems, and possible future development directions. Lippincott Williams & Wilkins 2022-02-05 2021-12-08 /pmc/articles/PMC8812665/ /pubmed/34995039 http://dx.doi.org/10.1097/CM9.0000000000001816 Text en Copyright © 2021 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Review Articles Li, Sicong Zhao, Ruiwei Zou, Haidong Artificial intelligence for diabetic retinopathy |
title | Artificial intelligence for diabetic retinopathy |
title_full | Artificial intelligence for diabetic retinopathy |
title_fullStr | Artificial intelligence for diabetic retinopathy |
title_full_unstemmed | Artificial intelligence for diabetic retinopathy |
title_short | Artificial intelligence for diabetic retinopathy |
title_sort | artificial intelligence for diabetic retinopathy |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812665/ https://www.ncbi.nlm.nih.gov/pubmed/34995039 http://dx.doi.org/10.1097/CM9.0000000000001816 |
work_keys_str_mv | AT lisicong artificialintelligencefordiabeticretinopathy AT zhaoruiwei artificialintelligencefordiabeticretinopathy AT zouhaidong artificialintelligencefordiabeticretinopathy |