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Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images
The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298137/ https://www.ncbi.nlm.nih.gov/pubmed/37372857 http://dx.doi.org/10.3390/healthcare11121739 |
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author | Li, Huimin Cao, Jing Grzybowski, Andrzej Jin, Kai Lou, Lixia Ye, Juan |
author_facet | Li, Huimin Cao, Jing Grzybowski, Andrzej Jin, Kai Lou, Lixia Ye, Juan |
author_sort | Li, Huimin |
collection | PubMed |
description | The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications. |
format | Online Article Text |
id | pubmed-10298137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102981372023-06-28 Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images Li, Huimin Cao, Jing Grzybowski, Andrzej Jin, Kai Lou, Lixia Ye, Juan Healthcare (Basel) Review The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications. MDPI 2023-06-13 /pmc/articles/PMC10298137/ /pubmed/37372857 http://dx.doi.org/10.3390/healthcare11121739 Text en © 2023 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 Li, Huimin Cao, Jing Grzybowski, Andrzej Jin, Kai Lou, Lixia Ye, Juan Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images |
title | Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images |
title_full | Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images |
title_fullStr | Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images |
title_full_unstemmed | Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images |
title_short | Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images |
title_sort | diagnosing systemic disorders with ai algorithms based on ocular images |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298137/ https://www.ncbi.nlm.nih.gov/pubmed/37372857 http://dx.doi.org/10.3390/healthcare11121739 |
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