Age-group determination of living individuals using first molar images based on artificial intelligence

Abstract Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a co...

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Autores principales:	Seunghyeon Kim, Yeon-Hee Lee, Yung-Kyun Noh, Frank C. Park, Q.-Schick Auh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/67fa27ace23e44b5bb94fa0c623c8060
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spelling	oai:doaj.org-article:67fa27ace23e44b5bb94fa0c623c80602021-12-02T14:01:37ZAge-group determination of living individuals using first molar images based on artificial intelligence10.1038/s41598-020-80182-82045-2322https://doaj.org/article/67fa27ace23e44b5bb94fa0c623c80602021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80182-8https://doaj.org/toc/2045-2322Abstract Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.Seunghyeon KimYeon-Hee LeeYung-Kyun NohFrank C. ParkQ.-Schick AuhNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Seunghyeon Kim Yeon-Hee Lee Yung-Kyun Noh Frank C. Park Q.-Schick Auh Age-group determination of living individuals using first molar images based on artificial intelligence
description	Abstract Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.
format	article
author	Seunghyeon Kim Yeon-Hee Lee Yung-Kyun Noh Frank C. Park Q.-Schick Auh
author_facet	Seunghyeon Kim Yeon-Hee Lee Yung-Kyun Noh Frank C. Park Q.-Schick Auh
author_sort	Seunghyeon Kim
title	Age-group determination of living individuals using first molar images based on artificial intelligence
title_short	Age-group determination of living individuals using first molar images based on artificial intelligence
title_full	Age-group determination of living individuals using first molar images based on artificial intelligence
title_fullStr	Age-group determination of living individuals using first molar images based on artificial intelligence
title_full_unstemmed	Age-group determination of living individuals using first molar images based on artificial intelligence
title_sort	age-group determination of living individuals using first molar images based on artificial intelligence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/67fa27ace23e44b5bb94fa0c623c8060
work_keys_str_mv	AT seunghyeonkim agegroupdeterminationoflivingindividualsusingfirstmolarimagesbasedonartificialintelligence AT yeonheelee agegroupdeterminationoflivingindividualsusingfirstmolarimagesbasedonartificialintelligence AT yungkyunnoh agegroupdeterminationoflivingindividualsusingfirstmolarimagesbasedonartificialintelligence AT frankcpark agegroupdeterminationoflivingindividualsusingfirstmolarimagesbasedonartificialintelligence AT qschickauh agegroupdeterminationoflivingindividualsusingfirstmolarimagesbasedonartificialintelligence
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Age-group determination of living individuals using first molar images based on artificial intelligence

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