Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Sicong, Zhao, Ruiwei, Zou, Haidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
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
_version_ 1784644702268555264
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