Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

RESULTS: The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. CONCLUSIONS: Here we demonstrate that superior diagnostic accuracy c...

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Autores principales: Vaghefi, Ehsan, Hill, Sophie, Kersten, Hannah M., Squirrell, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201607/
https://www.ncbi.nlm.nih.gov/pubmed/32411434
http://dx.doi.org/10.1155/2020/7493419
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author Vaghefi, Ehsan
Hill, Sophie
Kersten, Hannah M.
Squirrell, David
author_facet Vaghefi, Ehsan
Hill, Sophie
Kersten, Hannah M.
Squirrell, David
author_sort Vaghefi, Ehsan
collection PubMed
description RESULTS: The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. CONCLUSIONS: Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.
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spelling pubmed-72016072020-05-14 Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study Vaghefi, Ehsan Hill, Sophie Kersten, Hannah M. Squirrell, David J Ophthalmol Research Article RESULTS: The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. CONCLUSIONS: Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis. Hindawi 2020-01-13 /pmc/articles/PMC7201607/ /pubmed/32411434 http://dx.doi.org/10.1155/2020/7493419 Text en Copyright © 2020 Ehsan Vaghefi et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Vaghefi, Ehsan
Hill, Sophie
Kersten, Hannah M.
Squirrell, David
Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study
title Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study
title_full Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study
title_fullStr Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study
title_full_unstemmed Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study
title_short Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study
title_sort multimodal retinal image analysis via deep learning for the diagnosis of intermediate dry age-related macular degeneration: a feasibility study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201607/
https://www.ncbi.nlm.nih.gov/pubmed/32411434
http://dx.doi.org/10.1155/2020/7493419
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