Age estimation by radiomics analysis of mandibular condylar cone beam computed tomography images.

Abstract

Objectives: The aim of this study was to estimate the legal age using the parameters obtained from radiomic analysis of the mandibular condyle in cone beam computed tomography (CBCT) images.

Material and methods: The study group consisted of 300 mandibular condyles, which were categorized into six groups based on the age of the patients: 8-11 years, 12-14 years, 15-17 years, 18-20 years, 21-23 years, and over 24 years. Each patient's condyle was segmented individually using the 3D Slicer program. Radiomic features were extracted from the segmented images using the SlicerRadiomics plugin. Subsequently, three distinct models were developed with reference to three specific subgroups of the 12-14 age group, 15-17 age group, 18-20 age group and the efficacy of radiomic features in predicting the age of the patient was evaluated.

Results: The ROC analysis of the three radiomics scores (RS) yielded AUC values of 0.927, 0.860, and 0.769 for RS_12-14, RS_15-17, and RS_18-20, respectively. The RS_12-14 model exhibited the highest sensitivity and specificity values among the models, with 88% and 84.4%, respectively.

Conclusion: Among the radiomic features extracted from the mandibular condyle in CBCT images, the most significant features, identified based on developed models and their respective coefficients, can be applied to estimate patients' ages. Future studies hold substantial potential for advancing this method, particularly in automating both the segmentation process and the derivation of formulae for age estimation. The use of radiomic features for age prediction presents a promising alternative method for developing fully automated systems in clinical practice.