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Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram

The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to differentiate OSA as anatomical features in lateral ce...

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Autores principales: Kim, Min-Jung, Jeong, Jiheon, Lee, Jung-Wook, Kim, In-Hwan, Park, Jae-Woo, Roh, Jae-Yon, Kim, Namkug, Kim, Su-Jung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584979/
https://www.ncbi.nlm.nih.gov/pubmed/37853030
http://dx.doi.org/10.1038/s41598-023-42880-x
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author Kim, Min-Jung
Jeong, Jiheon
Lee, Jung-Wook
Kim, In-Hwan
Park, Jae-Woo
Roh, Jae-Yon
Kim, Namkug
Kim, Su-Jung
author_facet Kim, Min-Jung
Jeong, Jiheon
Lee, Jung-Wook
Kim, In-Hwan
Park, Jae-Woo
Roh, Jae-Yon
Kim, Namkug
Kim, Su-Jung
author_sort Kim, Min-Jung
collection PubMed
description The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to differentiate OSA as anatomical features in lateral cephalogram. Moreover, since the imaging devices used by each hospital could be different, there is a need to overcome modality difference of radiography. Therefore, we proposed a deep learning model with knowledge distillation to classify patients into OSA and non-OSA groups using the lateral cephalogram and to overcome modality differences simultaneously. Lateral cephalograms of 500 OSA patients and 498 non-OSA patients from two different devices were included. ResNet-50 and ResNet-50 with a feature-based knowledge distillation models were trained and their performances of classification were compared. Through the knowledge distillation, area under receiver operating characteristic curve analysis and gradient-weighted class activation mapping of knowledge distillation model exhibits high performance without being deceived by features caused by modality differences. By checking the probability values predicting OSA, an improvement in overcoming the modality differences was observed, which could be applied in the actual clinical situation.
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spelling pubmed-105849792023-10-20 Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram Kim, Min-Jung Jeong, Jiheon Lee, Jung-Wook Kim, In-Hwan Park, Jae-Woo Roh, Jae-Yon Kim, Namkug Kim, Su-Jung Sci Rep Article The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to differentiate OSA as anatomical features in lateral cephalogram. Moreover, since the imaging devices used by each hospital could be different, there is a need to overcome modality difference of radiography. Therefore, we proposed a deep learning model with knowledge distillation to classify patients into OSA and non-OSA groups using the lateral cephalogram and to overcome modality differences simultaneously. Lateral cephalograms of 500 OSA patients and 498 non-OSA patients from two different devices were included. ResNet-50 and ResNet-50 with a feature-based knowledge distillation models were trained and their performances of classification were compared. Through the knowledge distillation, area under receiver operating characteristic curve analysis and gradient-weighted class activation mapping of knowledge distillation model exhibits high performance without being deceived by features caused by modality differences. By checking the probability values predicting OSA, an improvement in overcoming the modality differences was observed, which could be applied in the actual clinical situation. Nature Publishing Group UK 2023-10-18 /pmc/articles/PMC10584979/ /pubmed/37853030 http://dx.doi.org/10.1038/s41598-023-42880-x 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
Kim, Min-Jung
Jeong, Jiheon
Lee, Jung-Wook
Kim, In-Hwan
Park, Jae-Woo
Roh, Jae-Yon
Kim, Namkug
Kim, Su-Jung
Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
title Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
title_full Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
title_fullStr Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
title_full_unstemmed Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
title_short Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
title_sort screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584979/
https://www.ncbi.nlm.nih.gov/pubmed/37853030
http://dx.doi.org/10.1038/s41598-023-42880-x
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