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A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images

BACKGROUND: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. METHOD: A total of 6393 OME otoscopic images from three centers were used to develop a...

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Autores principales: Zeng, Junbo, Deng, Wenting, Yu, Jingang, Xiao, Lichao, Chen, Suijun, Zhang, Xueyuan, Zeng, Linqi, Chen, Donglang, Li, Peng, Chen, Yubin, Zhang, Hongzheng, Shu, Fan, Wu, Minjian, Su, Yuejia, Li, Yuanqing, Cai, Yuexin, Zheng, Yiqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988777/
https://www.ncbi.nlm.nih.gov/pubmed/36227348
http://dx.doi.org/10.1007/s00405-022-07632-z
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author Zeng, Junbo
Deng, Wenting
Yu, Jingang
Xiao, Lichao
Chen, Suijun
Zhang, Xueyuan
Zeng, Linqi
Chen, Donglang
Li, Peng
Chen, Yubin
Zhang, Hongzheng
Shu, Fan
Wu, Minjian
Su, Yuejia
Li, Yuanqing
Cai, Yuexin
Zheng, Yiqing
author_facet Zeng, Junbo
Deng, Wenting
Yu, Jingang
Xiao, Lichao
Chen, Suijun
Zhang, Xueyuan
Zeng, Linqi
Chen, Donglang
Li, Peng
Chen, Yubin
Zhang, Hongzheng
Shu, Fan
Wu, Minjian
Su, Yuejia
Li, Yuanqing
Cai, Yuexin
Zheng, Yiqing
author_sort Zeng, Junbo
collection PubMed
description BACKGROUND: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. METHOD: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. RESULTS: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. CONCLUSION: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME.
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spelling pubmed-99887772023-03-08 A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images Zeng, Junbo Deng, Wenting Yu, Jingang Xiao, Lichao Chen, Suijun Zhang, Xueyuan Zeng, Linqi Chen, Donglang Li, Peng Chen, Yubin Zhang, Hongzheng Shu, Fan Wu, Minjian Su, Yuejia Li, Yuanqing Cai, Yuexin Zheng, Yiqing Eur Arch Otorhinolaryngol Otology BACKGROUND: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. METHOD: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. RESULTS: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. CONCLUSION: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME. Springer Berlin Heidelberg 2022-10-13 2023 /pmc/articles/PMC9988777/ /pubmed/36227348 http://dx.doi.org/10.1007/s00405-022-07632-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Otology
Zeng, Junbo
Deng, Wenting
Yu, Jingang
Xiao, Lichao
Chen, Suijun
Zhang, Xueyuan
Zeng, Linqi
Chen, Donglang
Li, Peng
Chen, Yubin
Zhang, Hongzheng
Shu, Fan
Wu, Minjian
Su, Yuejia
Li, Yuanqing
Cai, Yuexin
Zheng, Yiqing
A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images
title A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images
title_full A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images
title_fullStr A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images
title_full_unstemmed A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images
title_short A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images
title_sort deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images
topic Otology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988777/
https://www.ncbi.nlm.nih.gov/pubmed/36227348
http://dx.doi.org/10.1007/s00405-022-07632-z
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