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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Lippincott Williams & Wilkins
2023
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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. |
format | Online Article Text |
id | pubmed-10597411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
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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