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Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning
PURPOSE: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs. METHODS: We developed a deep learning construct to identify uveitis using refere...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718814/ https://www.ncbi.nlm.nih.gov/pubmed/33294300 http://dx.doi.org/10.1167/tvst.9.2.59 |
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author | Sun, Jian Huang, Xiaoqin Egwuagu, Charles Badr, Youakim Dryden, Stephen Charles Fowler, Brian Thomas Yousefi, Siamak |
author_facet | Sun, Jian Huang, Xiaoqin Egwuagu, Charles Badr, Youakim Dryden, Stephen Charles Fowler, Brian Thomas Yousefi, Siamak |
author_sort | Sun, Jian |
collection | PubMed |
description | PURPOSE: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs. METHODS: We developed a deep learning construct to identify uveitis using reference mouse fundus images and further categorized the severity levels of disease into mild and severe EAU. We evaluated the performance of the model using the area under the receiver operating characteristic curve (AUC) and confusion matrices. We further assessed the clinical relevance of the model by visualizing the principal components of features at different layers and through the use of gradient-weighted class activation maps, which presented retinal regions having the most significant influence on the model. RESULTS: Our model was trained, validated, and tested on 1500 fundus images (training, 1200; validation, 150; testing, 150) and achieved an average AUC of 0.98 for identifying the normal, trace (small and local lesions), and disease classes (large and spreading lesions). The AUCs of the model using an independent subset with 180 images were 1.00 (95% confidence interval [CI], 0.99–1.00), 0.97 (95% CI, 0.94–0.99), and 0.96 (95% CI, 0.90–1.00) for the normal, trace and disease classes, respectively. CONCLUSIONS: The proposed deep learning model is able to identify three severity levels of EAU with high accuracy. The model also achieved high accuracy on independent validation subsets, reflecting a substantial degree of generalizability. TRANSLATIONAL RELEVANCE: The proposed model represents an important new tool for use in animal medical research and provides a step toward clinical uveitis identification in clinical practice. |
format | Online Article Text |
id | pubmed-7718814 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-77188142020-12-07 Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning Sun, Jian Huang, Xiaoqin Egwuagu, Charles Badr, Youakim Dryden, Stephen Charles Fowler, Brian Thomas Yousefi, Siamak Transl Vis Sci Technol Special Issue PURPOSE: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs. METHODS: We developed a deep learning construct to identify uveitis using reference mouse fundus images and further categorized the severity levels of disease into mild and severe EAU. We evaluated the performance of the model using the area under the receiver operating characteristic curve (AUC) and confusion matrices. We further assessed the clinical relevance of the model by visualizing the principal components of features at different layers and through the use of gradient-weighted class activation maps, which presented retinal regions having the most significant influence on the model. RESULTS: Our model was trained, validated, and tested on 1500 fundus images (training, 1200; validation, 150; testing, 150) and achieved an average AUC of 0.98 for identifying the normal, trace (small and local lesions), and disease classes (large and spreading lesions). The AUCs of the model using an independent subset with 180 images were 1.00 (95% confidence interval [CI], 0.99–1.00), 0.97 (95% CI, 0.94–0.99), and 0.96 (95% CI, 0.90–1.00) for the normal, trace and disease classes, respectively. CONCLUSIONS: The proposed deep learning model is able to identify three severity levels of EAU with high accuracy. The model also achieved high accuracy on independent validation subsets, reflecting a substantial degree of generalizability. TRANSLATIONAL RELEVANCE: The proposed model represents an important new tool for use in animal medical research and provides a step toward clinical uveitis identification in clinical practice. The Association for Research in Vision and Ophthalmology 2020-12-02 /pmc/articles/PMC7718814/ /pubmed/33294300 http://dx.doi.org/10.1167/tvst.9.2.59 Text en Copyright 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Special Issue Sun, Jian Huang, Xiaoqin Egwuagu, Charles Badr, Youakim Dryden, Stephen Charles Fowler, Brian Thomas Yousefi, Siamak Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning |
title | Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning |
title_full | Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning |
title_fullStr | Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning |
title_full_unstemmed | Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning |
title_short | Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning |
title_sort | identifying mouse autoimmune uveitis from fundus photographs using deep learning |
topic | Special Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718814/ https://www.ncbi.nlm.nih.gov/pubmed/33294300 http://dx.doi.org/10.1167/tvst.9.2.59 |
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