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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-10663469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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