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Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study

Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using thos...

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Autores principales: Jeon, Kug Jin, Kim, Young Hyun, Choi, Hanseung, Ha, Eun-Gyu, Jeong, Hui, Han, Sang-Sun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851527/
https://www.ncbi.nlm.nih.gov/pubmed/36656878
http://dx.doi.org/10.1371/journal.pone.0280523
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author Jeon, Kug Jin
Kim, Young Hyun
Choi, Hanseung
Ha, Eun-Gyu
Jeong, Hui
Han, Sang-Sun
author_facet Jeon, Kug Jin
Kim, Young Hyun
Choi, Hanseung
Ha, Eun-Gyu
Jeong, Hui
Han, Sang-Sun
author_sort Jeon, Kug Jin
collection PubMed
description Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers. CBCT images from 85 subjects were divided into eight age groups for four legal age classifications: ≤17 and ≥18 years old groups (18-year age classification), ≤18 and ≥19 years old groups (19-year age classification), ≤19 and ≥20 years old groups (20-year age classification) and ≤20 and ≥21 years old groups (21-year age classification). The condylar heads were manually segmented by an expert. In total, 127 radiomics features were extracted from the segmented area of each condylar head. The random forest (RF) method was utilized to select features and develop the age classification model for four legal ages. After sorting features in descending order of importance, the top 10 extracted features were used. The 21-year age classification model showed the best performance, with an accuracy of 91.18%, sensitivity of 80%, and specificity of 95.83%. Radiomics features of the condylar head using CBCT showed the possibility of age estimation, and the selected features were useful as imaging biomarkers.
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spelling pubmed-98515272023-01-20 Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study Jeon, Kug Jin Kim, Young Hyun Choi, Hanseung Ha, Eun-Gyu Jeong, Hui Han, Sang-Sun PLoS One Research Article Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers. CBCT images from 85 subjects were divided into eight age groups for four legal age classifications: ≤17 and ≥18 years old groups (18-year age classification), ≤18 and ≥19 years old groups (19-year age classification), ≤19 and ≥20 years old groups (20-year age classification) and ≤20 and ≥21 years old groups (21-year age classification). The condylar heads were manually segmented by an expert. In total, 127 radiomics features were extracted from the segmented area of each condylar head. The random forest (RF) method was utilized to select features and develop the age classification model for four legal ages. After sorting features in descending order of importance, the top 10 extracted features were used. The 21-year age classification model showed the best performance, with an accuracy of 91.18%, sensitivity of 80%, and specificity of 95.83%. Radiomics features of the condylar head using CBCT showed the possibility of age estimation, and the selected features were useful as imaging biomarkers. Public Library of Science 2023-01-19 /pmc/articles/PMC9851527/ /pubmed/36656878 http://dx.doi.org/10.1371/journal.pone.0280523 Text en © 2023 Jeon et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jeon, Kug Jin
Kim, Young Hyun
Choi, Hanseung
Ha, Eun-Gyu
Jeong, Hui
Han, Sang-Sun
Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study
title Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study
title_full Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study
title_fullStr Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study
title_full_unstemmed Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study
title_short Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study
title_sort radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: a pilot study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851527/
https://www.ncbi.nlm.nih.gov/pubmed/36656878
http://dx.doi.org/10.1371/journal.pone.0280523
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