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Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468161/ https://www.ncbi.nlm.nih.gov/pubmed/34460801 http://dx.doi.org/10.3390/jimaging7090165 |
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author | Lakshminarayanan, Vasudevan Kheradfallah, Hoda Sarkar, Arya Jothi Balaji, Janarthanam |
author_facet | Lakshminarayanan, Vasudevan Kheradfallah, Hoda Sarkar, Arya Jothi Balaji, Janarthanam |
author_sort | Lakshminarayanan, Vasudevan |
collection | PubMed |
description | Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented. |
format | Online Article Text |
id | pubmed-8468161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84681612021-10-28 Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey Lakshminarayanan, Vasudevan Kheradfallah, Hoda Sarkar, Arya Jothi Balaji, Janarthanam J Imaging Review Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented. MDPI 2021-08-27 /pmc/articles/PMC8468161/ /pubmed/34460801 http://dx.doi.org/10.3390/jimaging7090165 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Lakshminarayanan, Vasudevan Kheradfallah, Hoda Sarkar, Arya Jothi Balaji, Janarthanam Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey |
title | Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey |
title_full | Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey |
title_fullStr | Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey |
title_full_unstemmed | Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey |
title_short | Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey |
title_sort | automated detection and diagnosis of diabetic retinopathy: a comprehensive survey |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468161/ https://www.ncbi.nlm.nih.gov/pubmed/34460801 http://dx.doi.org/10.3390/jimaging7090165 |
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