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Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs

Accurate image interpretation of Waters’ and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters’ and Caldwell views. The datasets were selected for the traini...

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Autores principales: Jeon, Yejin, Lee, Kyeorye, Sunwoo, Leonard, Choi, Dongjun, Oh, Dong Yul, Lee, Kyong Joon, Kim, Youngjune, Kim, Jeong-Whun, Cho, Se Jin, Baik, Sung Hyun, Yoo, Roh-eul, Bae, Yun Jung, Choi, Byung Se, Jung, Cheolkyu, Kim, Jae Hyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914751/
https://www.ncbi.nlm.nih.gov/pubmed/33562764
http://dx.doi.org/10.3390/diagnostics11020250
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author Jeon, Yejin
Lee, Kyeorye
Sunwoo, Leonard
Choi, Dongjun
Oh, Dong Yul
Lee, Kyong Joon
Kim, Youngjune
Kim, Jeong-Whun
Cho, Se Jin
Baik, Sung Hyun
Yoo, Roh-eul
Bae, Yun Jung
Choi, Byung Se
Jung, Cheolkyu
Kim, Jae Hyoung
author_facet Jeon, Yejin
Lee, Kyeorye
Sunwoo, Leonard
Choi, Dongjun
Oh, Dong Yul
Lee, Kyong Joon
Kim, Youngjune
Kim, Jeong-Whun
Cho, Se Jin
Baik, Sung Hyun
Yoo, Roh-eul
Bae, Yun Jung
Choi, Byung Se
Jung, Cheolkyu
Kim, Jae Hyoung
author_sort Jeon, Yejin
collection PubMed
description Accurate image interpretation of Waters’ and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters’ and Caldwell views. The datasets were selected for the training and validation set (n = 1403, sinusitis% = 34.3%) and the test set (n = 132, sinusitis% = 29.5%) by temporal separation. The algorithm can simultaneously detect and classify each paranasal sinus using both Waters’ and Caldwell views without manual cropping. Single- and multi-view models were compared. Our proposed algorithm satisfactorily diagnosed frontal, ethmoid, and maxillary sinusitis on both Waters’ and Caldwell views (area under the curve (AUC), 0.71 (95% confidence interval, 0.62–0.80), 0.78 (0.72–0.85), and 0.88 (0.84–0.92), respectively). The one-sided DeLong’s test was used to compare the AUCs, and the Obuchowski–Rockette model was used to pool the AUCs of the radiologists. The algorithm yielded a higher AUC than radiologists for ethmoid and maxillary sinusitis (p = 0.012 and 0.013, respectively). The multi-view model also exhibited a higher AUC than the single Waters’ view model for maxillary sinusitis (p = 0.038). Therefore, our algorithm showed diagnostic performances comparable to radiologists and enhanced the value of radiography as a first-line imaging modality in assessing multiple sinusitis.
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spelling pubmed-79147512021-03-01 Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs Jeon, Yejin Lee, Kyeorye Sunwoo, Leonard Choi, Dongjun Oh, Dong Yul Lee, Kyong Joon Kim, Youngjune Kim, Jeong-Whun Cho, Se Jin Baik, Sung Hyun Yoo, Roh-eul Bae, Yun Jung Choi, Byung Se Jung, Cheolkyu Kim, Jae Hyoung Diagnostics (Basel) Article Accurate image interpretation of Waters’ and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters’ and Caldwell views. The datasets were selected for the training and validation set (n = 1403, sinusitis% = 34.3%) and the test set (n = 132, sinusitis% = 29.5%) by temporal separation. The algorithm can simultaneously detect and classify each paranasal sinus using both Waters’ and Caldwell views without manual cropping. Single- and multi-view models were compared. Our proposed algorithm satisfactorily diagnosed frontal, ethmoid, and maxillary sinusitis on both Waters’ and Caldwell views (area under the curve (AUC), 0.71 (95% confidence interval, 0.62–0.80), 0.78 (0.72–0.85), and 0.88 (0.84–0.92), respectively). The one-sided DeLong’s test was used to compare the AUCs, and the Obuchowski–Rockette model was used to pool the AUCs of the radiologists. The algorithm yielded a higher AUC than radiologists for ethmoid and maxillary sinusitis (p = 0.012 and 0.013, respectively). The multi-view model also exhibited a higher AUC than the single Waters’ view model for maxillary sinusitis (p = 0.038). Therefore, our algorithm showed diagnostic performances comparable to radiologists and enhanced the value of radiography as a first-line imaging modality in assessing multiple sinusitis. MDPI 2021-02-05 /pmc/articles/PMC7914751/ /pubmed/33562764 http://dx.doi.org/10.3390/diagnostics11020250 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jeon, Yejin
Lee, Kyeorye
Sunwoo, Leonard
Choi, Dongjun
Oh, Dong Yul
Lee, Kyong Joon
Kim, Youngjune
Kim, Jeong-Whun
Cho, Se Jin
Baik, Sung Hyun
Yoo, Roh-eul
Bae, Yun Jung
Choi, Byung Se
Jung, Cheolkyu
Kim, Jae Hyoung
Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs
title Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs
title_full Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs
title_fullStr Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs
title_full_unstemmed Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs
title_short Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs
title_sort deep learning for diagnosis of paranasal sinusitis using multi-view radiographs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914751/
https://www.ncbi.nlm.nih.gov/pubmed/33562764
http://dx.doi.org/10.3390/diagnostics11020250
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