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Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review
The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have be...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821402/ https://www.ncbi.nlm.nih.gov/pubmed/36614953 http://dx.doi.org/10.3390/jcm12010152 |
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author | Wu, Jo-Hsuan Liu, Tin Yan Alvin |
author_facet | Wu, Jo-Hsuan Liu, Tin Yan Alvin |
author_sort | Wu, Jo-Hsuan |
collection | PubMed |
description | The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images—most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, “super-human” predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes. |
format | Online Article Text |
id | pubmed-9821402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98214022023-01-07 Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review Wu, Jo-Hsuan Liu, Tin Yan Alvin J Clin Med Review The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images—most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, “super-human” predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes. MDPI 2022-12-24 /pmc/articles/PMC9821402/ /pubmed/36614953 http://dx.doi.org/10.3390/jcm12010152 Text en © 2022 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 Wu, Jo-Hsuan Liu, Tin Yan Alvin Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review |
title | Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review |
title_full | Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review |
title_fullStr | Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review |
title_full_unstemmed | Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review |
title_short | Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review |
title_sort | application of deep learning to retinal-image-based oculomics for evaluation of systemic health: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821402/ https://www.ncbi.nlm.nih.gov/pubmed/36614953 http://dx.doi.org/10.3390/jcm12010152 |
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