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Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status

Background: Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. This study is aimed to develop an artificial intelligence (AI) system to automatically determine the CVM status and evaluate the AI performance. Methods: A total of 1080 cephalom...

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Autores principales: Zhou, Jing, Zhou, Hong, Pu, Lingling, Gao, Yanzi, Tang, Ziwei, Yang, Yi, You, Meng, Yang, Zheng, Lai, Wenli, Long, Hu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700528/
https://www.ncbi.nlm.nih.gov/pubmed/34943436
http://dx.doi.org/10.3390/diagnostics11122200
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author Zhou, Jing
Zhou, Hong
Pu, Lingling
Gao, Yanzi
Tang, Ziwei
Yang, Yi
You, Meng
Yang, Zheng
Lai, Wenli
Long, Hu
author_facet Zhou, Jing
Zhou, Hong
Pu, Lingling
Gao, Yanzi
Tang, Ziwei
Yang, Yi
You, Meng
Yang, Zheng
Lai, Wenli
Long, Hu
author_sort Zhou, Jing
collection PubMed
description Background: Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. This study is aimed to develop an artificial intelligence (AI) system to automatically determine the CVM status and evaluate the AI performance. Methods: A total of 1080 cephalometric radiographs, with the age of patients ranging from 6 to 22 years old, were included in the dataset (980 in training dataset and 100 in testing dataset). Two reference points and thirteen anatomical points were labelled and the cervical vertebral maturation staging (CS) was assessed by human examiners as gold standard. A convolutional neural network (CNN) model was built to train on 980 images and to test on 100 images. Statistical analysis was conducted to detect labelling differences between AI and human examiners, AI performance was also evaluated. Results: The mean labelling error between human examiners was 0.48 ± 0.12 mm. The mean labelling error between AI and human examiners was 0.36 ± 0.09 mm. In general, the agreement between AI results and the gold standard was good, with the intraclass correlation coefficient (ICC) value being up to 98%. Moreover, the accuracy of CVM staging was 71%. In terms of F1 score, CS6 stage (85%) ranked the highest accuracy. Conclusions: In this study, AI showed a good agreement with human examiners, being a useful and reliable tool in assessing the cervical vertebral maturation.
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spelling pubmed-87005282021-12-24 Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status Zhou, Jing Zhou, Hong Pu, Lingling Gao, Yanzi Tang, Ziwei Yang, Yi You, Meng Yang, Zheng Lai, Wenli Long, Hu Diagnostics (Basel) Article Background: Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. This study is aimed to develop an artificial intelligence (AI) system to automatically determine the CVM status and evaluate the AI performance. Methods: A total of 1080 cephalometric radiographs, with the age of patients ranging from 6 to 22 years old, were included in the dataset (980 in training dataset and 100 in testing dataset). Two reference points and thirteen anatomical points were labelled and the cervical vertebral maturation staging (CS) was assessed by human examiners as gold standard. A convolutional neural network (CNN) model was built to train on 980 images and to test on 100 images. Statistical analysis was conducted to detect labelling differences between AI and human examiners, AI performance was also evaluated. Results: The mean labelling error between human examiners was 0.48 ± 0.12 mm. The mean labelling error between AI and human examiners was 0.36 ± 0.09 mm. In general, the agreement between AI results and the gold standard was good, with the intraclass correlation coefficient (ICC) value being up to 98%. Moreover, the accuracy of CVM staging was 71%. In terms of F1 score, CS6 stage (85%) ranked the highest accuracy. Conclusions: In this study, AI showed a good agreement with human examiners, being a useful and reliable tool in assessing the cervical vertebral maturation. MDPI 2021-11-25 /pmc/articles/PMC8700528/ /pubmed/34943436 http://dx.doi.org/10.3390/diagnostics11122200 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhou, Jing
Zhou, Hong
Pu, Lingling
Gao, Yanzi
Tang, Ziwei
Yang, Yi
You, Meng
Yang, Zheng
Lai, Wenli
Long, Hu
Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status
title Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status
title_full Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status
title_fullStr Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status
title_full_unstemmed Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status
title_short Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status
title_sort development of an artificial intelligence system for the automatic evaluation of cervical vertebral maturation status
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700528/
https://www.ncbi.nlm.nih.gov/pubmed/34943436
http://dx.doi.org/10.3390/diagnostics11122200
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