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Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition

PURPOSE: To propose a five-category model for the automatic detection of myopic macular lesions to help grassroots medical institutions conduct preliminary screening of myopic macular lesions from limited number of color fundus images. METHODS: First, 1,750 fundus images of non-myopic retinal lesion...

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Autores principales: Wan, Cheng, Fang, Jiyi, Hua, Xiao, Chen, Lu, Zhang, Shaochong, Yang, Weihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10157024/
https://www.ncbi.nlm.nih.gov/pubmed/37152298
http://dx.doi.org/10.3389/fncom.2023.1169464
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author Wan, Cheng
Fang, Jiyi
Hua, Xiao
Chen, Lu
Zhang, Shaochong
Yang, Weihua
author_facet Wan, Cheng
Fang, Jiyi
Hua, Xiao
Chen, Lu
Zhang, Shaochong
Yang, Weihua
author_sort Wan, Cheng
collection PubMed
description PURPOSE: To propose a five-category model for the automatic detection of myopic macular lesions to help grassroots medical institutions conduct preliminary screening of myopic macular lesions from limited number of color fundus images. METHODS: First, 1,750 fundus images of non-myopic retinal lesions and four categories of pathological myopic maculopathy were collected, graded, and labeled. Subsequently, three five-classification models based on Vision Outlooker for Visual Recognition (VOLO), EfficientNetV2, and ResNet50 for detecting myopic maculopathy were trained with data-augmented images, and the diagnostic results of the different trained models were compared and analyzed. The main evaluation metrics were sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), area under the curve (AUC), kappa and accuracy, and receiver operating characteristic curve (ROC). RESULTS: The diagnostic accuracy of the VOLO-D2 model was 96.60% with a kappa value of 95.60%. All indicators used for the diagnosis of myopia-free macular degeneration were 100%. The sensitivity, NPV, specificity, and PPV for diagnosis of leopard fundus were 96.43, 98.33, 100, and 100%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of diffuse chorioretinal atrophy were 96.88, 98.59, 93.94, and 99.29%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of patchy chorioretinal atrophy were 92.31, 99.26, 97.30, and 97.81%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of macular atrophy were 100, 98.10, 84.21, and 100%, respectively. CONCLUSION: The VOLO-D2 model accurately identified myopia-free macular lesions and four pathological myopia-related macular lesions with high sensitivity and specificity. It can be used in screening pathological myopic macular lesions and can help ophthalmologists and primary medical institution providers complete the initial screening diagnosis of patients.
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spelling pubmed-101570242023-05-05 Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition Wan, Cheng Fang, Jiyi Hua, Xiao Chen, Lu Zhang, Shaochong Yang, Weihua Front Comput Neurosci Neuroscience PURPOSE: To propose a five-category model for the automatic detection of myopic macular lesions to help grassroots medical institutions conduct preliminary screening of myopic macular lesions from limited number of color fundus images. METHODS: First, 1,750 fundus images of non-myopic retinal lesions and four categories of pathological myopic maculopathy were collected, graded, and labeled. Subsequently, three five-classification models based on Vision Outlooker for Visual Recognition (VOLO), EfficientNetV2, and ResNet50 for detecting myopic maculopathy were trained with data-augmented images, and the diagnostic results of the different trained models were compared and analyzed. The main evaluation metrics were sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), area under the curve (AUC), kappa and accuracy, and receiver operating characteristic curve (ROC). RESULTS: The diagnostic accuracy of the VOLO-D2 model was 96.60% with a kappa value of 95.60%. All indicators used for the diagnosis of myopia-free macular degeneration were 100%. The sensitivity, NPV, specificity, and PPV for diagnosis of leopard fundus were 96.43, 98.33, 100, and 100%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of diffuse chorioretinal atrophy were 96.88, 98.59, 93.94, and 99.29%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of patchy chorioretinal atrophy were 92.31, 99.26, 97.30, and 97.81%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of macular atrophy were 100, 98.10, 84.21, and 100%, respectively. CONCLUSION: The VOLO-D2 model accurately identified myopia-free macular lesions and four pathological myopia-related macular lesions with high sensitivity and specificity. It can be used in screening pathological myopic macular lesions and can help ophthalmologists and primary medical institution providers complete the initial screening diagnosis of patients. Frontiers Media S.A. 2023-04-20 /pmc/articles/PMC10157024/ /pubmed/37152298 http://dx.doi.org/10.3389/fncom.2023.1169464 Text en Copyright © 2023 Wan, Fang, Hua, Chen, Zhang and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wan, Cheng
Fang, Jiyi
Hua, Xiao
Chen, Lu
Zhang, Shaochong
Yang, Weihua
Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition
title Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition
title_full Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition
title_fullStr Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition
title_full_unstemmed Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition
title_short Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition
title_sort automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10157024/
https://www.ncbi.nlm.nih.gov/pubmed/37152298
http://dx.doi.org/10.3389/fncom.2023.1169464
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