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

 Kug Jin Jeon  ;  Young Hyun Kim  ;  Hanseung Choi  ;  Eun-Gyu Ha  ;  Hui Jeong  ;  Sang-Sun Han 
 PLOS ONE, Vol.18(1) : e0280523, 2023-01 
Journal Title
Issue Date
Adolescent ; Adult ; Cone-Beam Computed Tomography* / methods ; Humans ; Machine Learning ; Mandibular Condyle* / diagnostic imaging ; Pilot Projects ; Retrospective Studies ; Young Adult
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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2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
Yonsei Authors
Jeon, Kug Jin(전국진) ORCID logo https://orcid.org/0000-0002-5862-2975
Han, Sang Sun(한상선) ORCID logo https://orcid.org/0000-0003-1775-7862
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