دورية أكاديمية
Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study.
العنوان: | Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study. |
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المؤلفون: | Jeon KJ; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea., Kim YH; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea., Choi H; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea., Ha EG; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea., Jeong H; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea., Han SS; Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea. |
المصدر: | PloS one [PLoS One] 2023 Jan 19; Vol. 18 (1), pp. e0280523. Date of Electronic Publication: 2023 Jan 19 (Print Publication: 2023). |
نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
اللغة: | English |
بيانات الدورية: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: San Francisco, CA : Public Library of Science |
مواضيع طبية MeSH: | Cone-Beam Computed Tomography*/methods , Mandibular Condyle*/diagnostic imaging, Humans ; Adolescent ; Young Adult ; Adult ; Pilot Projects ; Machine Learning ; Retrospective Studies |
مستخلص: | 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. Competing Interests: The authors have declared that no competing interests exist. (Copyright: © 2023 Jeon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
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تواريخ الأحداث: | Date Created: 20230119 Date Completed: 20230123 Latest Revision: 20230317 |
رمز التحديث: | 20231215 |
مُعرف محوري في PubMed: | PMC9851527 |
DOI: | 10.1371/journal.pone.0280523 |
PMID: | 36656878 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1932-6203 |
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DOI: | 10.1371/journal.pone.0280523 |