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Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion
Diabetic retinopathy (DR) remains one of the world’s frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216938/ https://www.ncbi.nlm.nih.gov/pubmed/37238179 http://dx.doi.org/10.3390/diagnostics13101694 |
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author | Farahat, Zineb Zrira, Nabila Souissi, Nissrine Benamar, Safia Belmekki, Mohammed Ngote, Mohamed Nabil Megdiche, Kawtar |
author_facet | Farahat, Zineb Zrira, Nabila Souissi, Nissrine Benamar, Safia Belmekki, Mohammed Ngote, Mohamed Nabil Megdiche, Kawtar |
author_sort | Farahat, Zineb |
collection | PubMed |
description | Diabetic retinopathy (DR) remains one of the world’s frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of human life and gradually transform medical practice. Insight into the condition of the retina is becoming more accessible thanks to major advancements in diagnostic technology. AI approaches can be used to assess lots of morphological datasets derived from digital images in a rapid and noninvasive manner. Computer-aided diagnosis tools for automatic detection of DR early-stage signs will ease the pressure on clinicians. In this work, we apply two methods to the color fundus images taken on-site at the Cheikh Zaïd Foundation’s Ophthalmic Center in Rabat to detect both exudates and hemorrhages. First, we apply the U-Net method to segment exudates and hemorrhages into red and green colors, respectively. Second, the You Look Only Once Version 5 (YOLOv5) method identifies the presence of hemorrhages and exudates in an image and predicts a probability for each bounding box. The segmentation proposed method obtained a specificity of 85%, a sensitivity of 85%, and a Dice score of 85%. The detection software successfully detected 100% of diabetic retinopathy signs, the expert doctor detected 99% of DR signs, and the resident doctor detected 84%. |
format | Online Article Text |
id | pubmed-10216938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102169382023-05-27 Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion Farahat, Zineb Zrira, Nabila Souissi, Nissrine Benamar, Safia Belmekki, Mohammed Ngote, Mohamed Nabil Megdiche, Kawtar Diagnostics (Basel) Article Diabetic retinopathy (DR) remains one of the world’s frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of human life and gradually transform medical practice. Insight into the condition of the retina is becoming more accessible thanks to major advancements in diagnostic technology. AI approaches can be used to assess lots of morphological datasets derived from digital images in a rapid and noninvasive manner. Computer-aided diagnosis tools for automatic detection of DR early-stage signs will ease the pressure on clinicians. In this work, we apply two methods to the color fundus images taken on-site at the Cheikh Zaïd Foundation’s Ophthalmic Center in Rabat to detect both exudates and hemorrhages. First, we apply the U-Net method to segment exudates and hemorrhages into red and green colors, respectively. Second, the You Look Only Once Version 5 (YOLOv5) method identifies the presence of hemorrhages and exudates in an image and predicts a probability for each bounding box. The segmentation proposed method obtained a specificity of 85%, a sensitivity of 85%, and a Dice score of 85%. The detection software successfully detected 100% of diabetic retinopathy signs, the expert doctor detected 99% of DR signs, and the resident doctor detected 84%. MDPI 2023-05-10 /pmc/articles/PMC10216938/ /pubmed/37238179 http://dx.doi.org/10.3390/diagnostics13101694 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Farahat, Zineb Zrira, Nabila Souissi, Nissrine Benamar, Safia Belmekki, Mohammed Ngote, Mohamed Nabil Megdiche, Kawtar Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion |
title | Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion |
title_full | Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion |
title_fullStr | Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion |
title_full_unstemmed | Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion |
title_short | Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion |
title_sort | application of deep learning methods in a moroccan ophthalmic center: analysis and discussion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216938/ https://www.ncbi.nlm.nih.gov/pubmed/37238179 http://dx.doi.org/10.3390/diagnostics13101694 |
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