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Differentiating malignant and benign eyelid lesions using deep learning
Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in compariso...
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/PMC10011394/ https://www.ncbi.nlm.nih.gov/pubmed/36914694 http://dx.doi.org/10.1038/s41598-023-30699-5 |
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author | Lee, Min Joung Yang, Min Kyu Khwarg, Sang In Oh, Eun Kyu Choi, Youn Joo Kim, Namju Choung, Hokyung Seo, Chang Won Ha, Yun Jong Cho, Min Ho Cho, Bum-Joo |
author_facet | Lee, Min Joung Yang, Min Kyu Khwarg, Sang In Oh, Eun Kyu Choi, Youn Joo Kim, Namju Choung, Hokyung Seo, Chang Won Ha, Yun Jong Cho, Min Ho Cho, Bum-Joo |
author_sort | Lee, Min Joung |
collection | PubMed |
description | Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0–89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8–90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers. |
format | Online Article Text |
id | pubmed-10011394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100113942023-03-15 Differentiating malignant and benign eyelid lesions using deep learning Lee, Min Joung Yang, Min Kyu Khwarg, Sang In Oh, Eun Kyu Choi, Youn Joo Kim, Namju Choung, Hokyung Seo, Chang Won Ha, Yun Jong Cho, Min Ho Cho, Bum-Joo Sci Rep Article Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0–89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8–90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers. Nature Publishing Group UK 2023-03-13 /pmc/articles/PMC10011394/ /pubmed/36914694 http://dx.doi.org/10.1038/s41598-023-30699-5 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 Lee, Min Joung Yang, Min Kyu Khwarg, Sang In Oh, Eun Kyu Choi, Youn Joo Kim, Namju Choung, Hokyung Seo, Chang Won Ha, Yun Jong Cho, Min Ho Cho, Bum-Joo Differentiating malignant and benign eyelid lesions using deep learning |
title | Differentiating malignant and benign eyelid lesions using deep learning |
title_full | Differentiating malignant and benign eyelid lesions using deep learning |
title_fullStr | Differentiating malignant and benign eyelid lesions using deep learning |
title_full_unstemmed | Differentiating malignant and benign eyelid lesions using deep learning |
title_short | Differentiating malignant and benign eyelid lesions using deep learning |
title_sort | differentiating malignant and benign eyelid lesions using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011394/ https://www.ncbi.nlm.nih.gov/pubmed/36914694 http://dx.doi.org/10.1038/s41598-023-30699-5 |
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