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Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy
Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559815/ https://www.ncbi.nlm.nih.gov/pubmed/36246896 http://dx.doi.org/10.3389/fendo.2022.946915 |
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author | Huang, Xuan Wang, Hui She, Chongyang Feng, Jing Liu, Xuhui Hu, Xiaofeng Chen, Li Tao, Yong |
author_facet | Huang, Xuan Wang, Hui She, Chongyang Feng, Jing Liu, Xuhui Hu, Xiaofeng Chen, Li Tao, Yong |
author_sort | Huang, Xuan |
collection | PubMed |
description | Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI. |
format | Online Article Text |
id | pubmed-9559815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95598152022-10-14 Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy Huang, Xuan Wang, Hui She, Chongyang Feng, Jing Liu, Xuhui Hu, Xiaofeng Chen, Li Tao, Yong Front Endocrinol (Lausanne) Endocrinology Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI. Frontiers Media S.A. 2022-09-29 /pmc/articles/PMC9559815/ /pubmed/36246896 http://dx.doi.org/10.3389/fendo.2022.946915 Text en Copyright © 2022 Huang, Wang, She, Feng, Liu, Hu, Chen and Tao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Huang, Xuan Wang, Hui She, Chongyang Feng, Jing Liu, Xuhui Hu, Xiaofeng Chen, Li Tao, Yong Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy |
title | Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy |
title_full | Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy |
title_fullStr | Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy |
title_full_unstemmed | Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy |
title_short | Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy |
title_sort | artificial intelligence promotes the diagnosis and screening of diabetic retinopathy |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559815/ https://www.ncbi.nlm.nih.gov/pubmed/36246896 http://dx.doi.org/10.3389/fendo.2022.946915 |
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