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

AI Article Synopsis

  • Legal age estimation is crucial, and this study uses radiomics to extract data from cone-beam computed tomography (CBCT) images of the mandibular condylar head for age classification.
  • The research aimed to classify individuals into four legal age categories (18, 19, 20, and 21 years old) using features derived from medical imaging, evaluating the effectiveness of machine learning models.
  • The findings indicated that the age classification model for 21 years achieved high accuracy (91.18%), demonstrating that radiomics features could serve as reliable imaging biomarkers for estimating legal age.

Article Abstract

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851527PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0280523PLOS

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