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Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates

Velopharyngeal insufficiency (VPI), which is the incomplete closure of the velopharyngeal valve during speech, is a typical poor outcome that should be evaluated after cleft palate repair. The interpretation of VPI considering both imaging analysis and perceptual evaluation is essential for further...

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Autores principales: Ha, Jeong Hyun, Lee, Haeyun, Kwon, Seok Min, Joo, Hyunjin, Lin, Guang, Kim, Deok-Yeol, Kim, Sukwha, Hwang, Jae Youn, Chung, Jee-Hyeok, Kong, Hyoun-Joong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10597411/
https://www.ncbi.nlm.nih.gov/pubmed/37815288
http://dx.doi.org/10.1097/SCS.0000000000009560
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author Ha, Jeong Hyun
Lee, Haeyun
Kwon, Seok Min
Joo, Hyunjin
Lin, Guang
Kim, Deok-Yeol
Kim, Sukwha
Hwang, Jae Youn
Chung, Jee-Hyeok
Kong, Hyoun-Joong
author_facet Ha, Jeong Hyun
Lee, Haeyun
Kwon, Seok Min
Joo, Hyunjin
Lin, Guang
Kim, Deok-Yeol
Kim, Sukwha
Hwang, Jae Youn
Chung, Jee-Hyeok
Kong, Hyoun-Joong
author_sort Ha, Jeong Hyun
collection PubMed
description Velopharyngeal insufficiency (VPI), which is the incomplete closure of the velopharyngeal valve during speech, is a typical poor outcome that should be evaluated after cleft palate repair. The interpretation of VPI considering both imaging analysis and perceptual evaluation is essential for further management. The authors retrospectively reviewed patients with repaired cleft palates who underwent assessment for velopharyngeal function, including both videofluoroscopic imaging and perceptual speech evaluation. The final diagnosis of VPI was made by plastic surgeons based on both assessment modalities. Deep learning techniques were applied for the diagnosis of VPI and compared with the human experts’ diagnostic results of videofluoroscopic imaging. In addition, the results of the deep learning techniques were compared with a speech pathologist’s diagnosis of perceptual evaluation to assess consistency with clinical symptoms. A total of 714 cases from January 2010 to June 2019 were reviewed. Six deep learning algorithms (VGGNet, ResNet, Xception, ResNext, DenseNet, and SENet) were trained using the obtained dataset. The area under the receiver operating characteristic curve of the algorithms ranged between 0.8758 and 0.9468 in the hold-out method and between 0.7992 and 0.8574 in the 5-fold cross-validation. Our findings demonstrated the deep learning algorithms performed comparable to experienced plastic surgeons in the diagnosis of VPI based on videofluoroscopic velopharyngeal imaging.
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spelling pubmed-105974112023-10-25 Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates Ha, Jeong Hyun Lee, Haeyun Kwon, Seok Min Joo, Hyunjin Lin, Guang Kim, Deok-Yeol Kim, Sukwha Hwang, Jae Youn Chung, Jee-Hyeok Kong, Hyoun-Joong J Craniofac Surg Original Articles Velopharyngeal insufficiency (VPI), which is the incomplete closure of the velopharyngeal valve during speech, is a typical poor outcome that should be evaluated after cleft palate repair. The interpretation of VPI considering both imaging analysis and perceptual evaluation is essential for further management. The authors retrospectively reviewed patients with repaired cleft palates who underwent assessment for velopharyngeal function, including both videofluoroscopic imaging and perceptual speech evaluation. The final diagnosis of VPI was made by plastic surgeons based on both assessment modalities. Deep learning techniques were applied for the diagnosis of VPI and compared with the human experts’ diagnostic results of videofluoroscopic imaging. In addition, the results of the deep learning techniques were compared with a speech pathologist’s diagnosis of perceptual evaluation to assess consistency with clinical symptoms. A total of 714 cases from January 2010 to June 2019 were reviewed. Six deep learning algorithms (VGGNet, ResNet, Xception, ResNext, DenseNet, and SENet) were trained using the obtained dataset. The area under the receiver operating characteristic curve of the algorithms ranged between 0.8758 and 0.9468 in the hold-out method and between 0.7992 and 0.8574 in the 5-fold cross-validation. Our findings demonstrated the deep learning algorithms performed comparable to experienced plastic surgeons in the diagnosis of VPI based on videofluoroscopic velopharyngeal imaging. Lippincott Williams & Wilkins 2023 2023-10-09 /pmc/articles/PMC10597411/ /pubmed/37815288 http://dx.doi.org/10.1097/SCS.0000000000009560 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of Mutaz B. Habal, MD. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/)
spellingShingle Original Articles
Ha, Jeong Hyun
Lee, Haeyun
Kwon, Seok Min
Joo, Hyunjin
Lin, Guang
Kim, Deok-Yeol
Kim, Sukwha
Hwang, Jae Youn
Chung, Jee-Hyeok
Kong, Hyoun-Joong
Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates
title Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates
title_full Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates
title_fullStr Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates
title_full_unstemmed Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates
title_short Deep Learning–Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates
title_sort deep learning–based diagnostic system for velopharyngeal insufficiency based on videofluoroscopy in patients with repaired cleft palates
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10597411/
https://www.ncbi.nlm.nih.gov/pubmed/37815288
http://dx.doi.org/10.1097/SCS.0000000000009560
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