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Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population

BACKGROUND: To establish the digital diagnostic templates by cluster analysis based on a set of cephalometric films and evaluate the outcome of the different treatment methods in the patients affiliated to the same cephalometric morphology template (CMT). These templates could be used for the automa...

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Autores principales: Su, Hong, Lu, Wenhsuan, Deng, Jingjing, Chen, Gui, Jiang, Ruoping, Wei, Yan, Zhang, Xiaoyun, Xu, Tianmin, Han, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842905/
https://www.ncbi.nlm.nih.gov/pubmed/35164789
http://dx.doi.org/10.1186/s13005-022-00309-2
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author Su, Hong
Lu, Wenhsuan
Deng, Jingjing
Chen, Gui
Jiang, Ruoping
Wei, Yan
Zhang, Xiaoyun
Xu, Tianmin
Han, Bing
author_facet Su, Hong
Lu, Wenhsuan
Deng, Jingjing
Chen, Gui
Jiang, Ruoping
Wei, Yan
Zhang, Xiaoyun
Xu, Tianmin
Han, Bing
author_sort Su, Hong
collection PubMed
description BACKGROUND: To establish the digital diagnostic templates by cluster analysis based on a set of cephalometric films and evaluate the outcome of the different treatment methods in the patients affiliated to the same cephalometric morphology template (CMT). These templates could be used for the automatic diagnosis of dentofacial deformities and prediction of treatment outcomes in the future. METHODS: In this study, we assessed the coordinates of 60 different landmarks on the cephalograms of 2249 patients (14.35 ± 4.99 years, range from 7 to 62) with dentofacial deformities. The cephalometric data were subjected to dentist for clustering without a priori pattern definitions to generate biologically informative CMTs. Three templates were selected to evaluate the treatment outcome of patients affiliated to the same CMT. RESULTS: The cluster analysis yielded 21 distinct groups. The total discriminant accuracy was 89.1%, while the cross-validation accuracy was 85.0%, showing that the clusters were robust. All CMTs were automatically created and drawn using a computer, based on the average coordinates of each cluster. Individuals affiliated to the same CMT showed similar dentofacial features. We also evaluated differences in the outcomes of patients affiliated to the same CMT. CONCLUSIONS: Our results demonstrated the utility of clustering methods for grouping dentofacial deformities with similar dentofacial features. Clustering methods can be used to evaluate the differences in the outcomes of patients affiliated to the same CMT, which has good clinical application value. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13005-022-00309-2.
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spelling pubmed-88429052022-02-16 Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population Su, Hong Lu, Wenhsuan Deng, Jingjing Chen, Gui Jiang, Ruoping Wei, Yan Zhang, Xiaoyun Xu, Tianmin Han, Bing Head Face Med Research BACKGROUND: To establish the digital diagnostic templates by cluster analysis based on a set of cephalometric films and evaluate the outcome of the different treatment methods in the patients affiliated to the same cephalometric morphology template (CMT). These templates could be used for the automatic diagnosis of dentofacial deformities and prediction of treatment outcomes in the future. METHODS: In this study, we assessed the coordinates of 60 different landmarks on the cephalograms of 2249 patients (14.35 ± 4.99 years, range from 7 to 62) with dentofacial deformities. The cephalometric data were subjected to dentist for clustering without a priori pattern definitions to generate biologically informative CMTs. Three templates were selected to evaluate the treatment outcome of patients affiliated to the same CMT. RESULTS: The cluster analysis yielded 21 distinct groups. The total discriminant accuracy was 89.1%, while the cross-validation accuracy was 85.0%, showing that the clusters were robust. All CMTs were automatically created and drawn using a computer, based on the average coordinates of each cluster. Individuals affiliated to the same CMT showed similar dentofacial features. We also evaluated differences in the outcomes of patients affiliated to the same CMT. CONCLUSIONS: Our results demonstrated the utility of clustering methods for grouping dentofacial deformities with similar dentofacial features. Clustering methods can be used to evaluate the differences in the outcomes of patients affiliated to the same CMT, which has good clinical application value. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13005-022-00309-2. BioMed Central 2022-02-14 /pmc/articles/PMC8842905/ /pubmed/35164789 http://dx.doi.org/10.1186/s13005-022-00309-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Su, Hong
Lu, Wenhsuan
Deng, Jingjing
Chen, Gui
Jiang, Ruoping
Wei, Yan
Zhang, Xiaoyun
Xu, Tianmin
Han, Bing
Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population
title Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population
title_full Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population
title_fullStr Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population
title_full_unstemmed Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population
title_short Development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of Chinese Han population
title_sort development of digital diagnostic templates by cluster analysis based on 2249 lateral cephalograms of chinese han population
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842905/
https://www.ncbi.nlm.nih.gov/pubmed/35164789
http://dx.doi.org/10.1186/s13005-022-00309-2
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