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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...

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Autores principales: Sun, Jian, Huang, Xiaoqin, Egwuagu, Charles, Badr, Youakim, Dryden, Stephen Charles, Fowler, Brian Thomas, Yousefi, Siamak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2020
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.
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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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