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Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study

Cholesteatoma is a progressive middle ear disease that can only be treated surgically but with a high recurrence rate. Depending on the extent of the disease, a surgical approach, such as microsurgery with a retroarticular incision or transcanal endoscopic surgery, is performed. However, the current...

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Autores principales: Takahashi, Masahiro, Noda, Katsuhiko, Yoshida, Kaname, Tsuchida, Keisuke, Yui, Ryosuke, Nakazawa, Takara, Kurihara, Sho, Baba, Akira, Motegi, Masaomi, Yamamoto, Kazuhisa, Yamamoto, Yutaka, Ojiri, Hiroya, Kojima, Hiromi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9529134/
https://www.ncbi.nlm.nih.gov/pubmed/36190937
http://dx.doi.org/10.1371/journal.pone.0273915
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author Takahashi, Masahiro
Noda, Katsuhiko
Yoshida, Kaname
Tsuchida, Keisuke
Yui, Ryosuke
Nakazawa, Takara
Kurihara, Sho
Baba, Akira
Motegi, Masaomi
Yamamoto, Kazuhisa
Yamamoto, Yutaka
Ojiri, Hiroya
Kojima, Hiromi
author_facet Takahashi, Masahiro
Noda, Katsuhiko
Yoshida, Kaname
Tsuchida, Keisuke
Yui, Ryosuke
Nakazawa, Takara
Kurihara, Sho
Baba, Akira
Motegi, Masaomi
Yamamoto, Kazuhisa
Yamamoto, Yutaka
Ojiri, Hiroya
Kojima, Hiromi
author_sort Takahashi, Masahiro
collection PubMed
description Cholesteatoma is a progressive middle ear disease that can only be treated surgically but with a high recurrence rate. Depending on the extent of the disease, a surgical approach, such as microsurgery with a retroarticular incision or transcanal endoscopic surgery, is performed. However, the current examination cannot sufficiently predict the progression before surgery, and changes in approach may be made during the surgery. Large amounts of data are typically required to train deep neural network models; however, the prevalence of cholesteatomas is low (1-in-25, 000). Developing analysis methods that improve the accuracy with such a small number of samples is an important issue for medical artificial intelligence (AI) research. This paper presents an AI-based system to automatically detect mastoid extensions using CT. This retrospective study included 164 patients (80 with mastoid extension and 84 without mastoid extension) who underwent surgery. This study adopted a relatively lightweight neural network model called MobileNetV2 to learn and predict the CT images of 164 patients. The training was performed with eight divided groups for cross-validation and was performed 24 times with each of the eight groups to verify accuracy fluctuations caused by randomly augmented learning. An evaluation was performed by each of the 24 single-trained models, and 24 sets of ensemble predictions with 23 models for 100% original size images and 400% zoomed images. Fifteen otolaryngologists diagnosed the images and compared the results. The average accuracy of predicting 400% zoomed images using ensemble prediction model was 81.14% (sensitivity = 84.95%, specificity = 77.33%). The average accuracy of the otolaryngologists was 73.41% (sensitivity, 83.17%; specificity, 64.13%), which was not affected by their clinical experiences. Noteworthily, despite the small number of cases, we were able to create a highly accurate AI. These findings represent an important first step in the automatic diagnosis of the cholesteatoma extension.
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spelling pubmed-95291342022-10-04 Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study Takahashi, Masahiro Noda, Katsuhiko Yoshida, Kaname Tsuchida, Keisuke Yui, Ryosuke Nakazawa, Takara Kurihara, Sho Baba, Akira Motegi, Masaomi Yamamoto, Kazuhisa Yamamoto, Yutaka Ojiri, Hiroya Kojima, Hiromi PLoS One Research Article Cholesteatoma is a progressive middle ear disease that can only be treated surgically but with a high recurrence rate. Depending on the extent of the disease, a surgical approach, such as microsurgery with a retroarticular incision or transcanal endoscopic surgery, is performed. However, the current examination cannot sufficiently predict the progression before surgery, and changes in approach may be made during the surgery. Large amounts of data are typically required to train deep neural network models; however, the prevalence of cholesteatomas is low (1-in-25, 000). Developing analysis methods that improve the accuracy with such a small number of samples is an important issue for medical artificial intelligence (AI) research. This paper presents an AI-based system to automatically detect mastoid extensions using CT. This retrospective study included 164 patients (80 with mastoid extension and 84 without mastoid extension) who underwent surgery. This study adopted a relatively lightweight neural network model called MobileNetV2 to learn and predict the CT images of 164 patients. The training was performed with eight divided groups for cross-validation and was performed 24 times with each of the eight groups to verify accuracy fluctuations caused by randomly augmented learning. An evaluation was performed by each of the 24 single-trained models, and 24 sets of ensemble predictions with 23 models for 100% original size images and 400% zoomed images. Fifteen otolaryngologists diagnosed the images and compared the results. The average accuracy of predicting 400% zoomed images using ensemble prediction model was 81.14% (sensitivity = 84.95%, specificity = 77.33%). The average accuracy of the otolaryngologists was 73.41% (sensitivity, 83.17%; specificity, 64.13%), which was not affected by their clinical experiences. Noteworthily, despite the small number of cases, we were able to create a highly accurate AI. These findings represent an important first step in the automatic diagnosis of the cholesteatoma extension. Public Library of Science 2022-10-03 /pmc/articles/PMC9529134/ /pubmed/36190937 http://dx.doi.org/10.1371/journal.pone.0273915 Text en © 2022 Takahashi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Takahashi, Masahiro
Noda, Katsuhiko
Yoshida, Kaname
Tsuchida, Keisuke
Yui, Ryosuke
Nakazawa, Takara
Kurihara, Sho
Baba, Akira
Motegi, Masaomi
Yamamoto, Kazuhisa
Yamamoto, Yutaka
Ojiri, Hiroya
Kojima, Hiromi
Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study
title Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study
title_full Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study
title_fullStr Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study
title_full_unstemmed Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study
title_short Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study
title_sort preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: a retrospective study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9529134/
https://www.ncbi.nlm.nih.gov/pubmed/36190937
http://dx.doi.org/10.1371/journal.pone.0273915
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