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Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy

To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patients with complete outer retinal and retinal pigmen...

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Autores principales: Chouraqui, Maxime, Crincoli, Emanuele, Miere, Alexandra, Meunier, Isabelle Anne, Souied, Eric H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663469/
https://www.ncbi.nlm.nih.gov/pubmed/37990107
http://dx.doi.org/10.1038/s41598-023-47854-7
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author Chouraqui, Maxime
Crincoli, Emanuele
Miere, Alexandra
Meunier, Isabelle Anne
Souied, Eric H.
author_facet Chouraqui, Maxime
Crincoli, Emanuele
Miere, Alexandra
Meunier, Isabelle Anne
Souied, Eric H.
author_sort Chouraqui, Maxime
collection PubMed
description To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patients with complete outer retinal and retinal pigment epithelium atrophy secondary to either EMAP (EMAP Group) or to dry age related macular degeneration (AMD group) were retrospectively selected. Fovea-centered posterior pole (30° × 30°) and 55° × 55° degree-field-of-view FAF images of sufficiently high quality were collected and used to train two different deep learning (DL) classifiers based on ResNet-101 design. Testing was performed on a set of images coming from a different center. A total of 300 patients were recruited, 135 belonging to EMAP group and 165 belonging to AMD group. The 30° × 30° FAF based DL classifier showed a sensitivity of 84.6% and a specificity of 85.3% for the diagnosis of EMAP. The 55° × 55° FAF based DL classifier showed a sensitivity of 90% and a specificity of 84.6%, a performance that was significantly higher than that of the 30° × 30° classifer (p = 0.037). Artificial intelligence can accurately distinguish between atrophy caused by AMD or by EMAP on FAF images. Its performance are improved using wide field acquisitions.
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spelling pubmed-106634692023-11-21 Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy Chouraqui, Maxime Crincoli, Emanuele Miere, Alexandra Meunier, Isabelle Anne Souied, Eric H. Sci Rep Article To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patients with complete outer retinal and retinal pigment epithelium atrophy secondary to either EMAP (EMAP Group) or to dry age related macular degeneration (AMD group) were retrospectively selected. Fovea-centered posterior pole (30° × 30°) and 55° × 55° degree-field-of-view FAF images of sufficiently high quality were collected and used to train two different deep learning (DL) classifiers based on ResNet-101 design. Testing was performed on a set of images coming from a different center. A total of 300 patients were recruited, 135 belonging to EMAP group and 165 belonging to AMD group. The 30° × 30° FAF based DL classifier showed a sensitivity of 84.6% and a specificity of 85.3% for the diagnosis of EMAP. The 55° × 55° FAF based DL classifier showed a sensitivity of 90% and a specificity of 84.6%, a performance that was significantly higher than that of the 30° × 30° classifer (p = 0.037). Artificial intelligence can accurately distinguish between atrophy caused by AMD or by EMAP on FAF images. Its performance are improved using wide field acquisitions. Nature Publishing Group UK 2023-11-21 /pmc/articles/PMC10663469/ /pubmed/37990107 http://dx.doi.org/10.1038/s41598-023-47854-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chouraqui, Maxime
Crincoli, Emanuele
Miere, Alexandra
Meunier, Isabelle Anne
Souied, Eric H.
Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy
title Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy
title_full Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy
title_fullStr Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy
title_full_unstemmed Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy
title_short Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy
title_sort deep learning model for automatic differentiation of emap from amd in macular atrophy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663469/
https://www.ncbi.nlm.nih.gov/pubmed/37990107
http://dx.doi.org/10.1038/s41598-023-47854-7
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