ADVANCING FORENSIC AGE ESTIMATION: INTEGRATING CBCT IMAGING WITH MACHINE LEARNING AND DEEP LEARNING APPROACHES

Abstract

Dental age estimation plays a vital role in forensic odontology, contributing to legal, clinical, and identification processes. While previous studies predominantly focused on maxillary teeth, mandibular teeth, known for their greater durability in adults, have been underexplored for this purpose. This research addresses this gap by utilizing cone beam computed tomography (CBCT) images of mandibular teeth alongside advanced machine learning and deep learning techniques to enhance predictive accuracy. The study utilized a dataset of 118 CBCT images collected for diagnostic purposes at the Jordan University of Science and Technology Dental Teaching Center. The dataset, comprising 50 males and 68 females with ages ranging from 18 to 72 years (mean age: 34.58 ± 14.20 years), was preprocessed to ensure consistency and reliability. A forward feature selection approach identified key age predictive features, including periodontal recession, axial crown area, and pulp-tooth volume ratio. Notably, axial plane measurements emerged as particularly informative, with three of the top ten age-correlated features derived from axial imaging. The machine learning framework was designed to accommodate any of four mandibular teeth (canines and second premolars), enhancing flexibility in forensic scenarios where specific teeth may be missing, damaged, or impacted. An ensemble model combining Decision Tree, K-Nearest Neighbors, XGBoost, and CatBoost regressors achieved a mean absolute error (MAE) of 4.19 years, outperforming individual regression models. To further explore automated dental age estimation, deep learning models were applied to 2,384 orthopantomogram (OPG) images, including both original OPGs and reconstructed OPGs derived from CBCT scans. Among the 37 evaluated deep learning models, DenseNet169 and ResNeXt101-64x4d demonstrated the highest accuracy, achieving an MAE of 5.35 years when tested on the 118 CBCT dataset, a result comparable to traditional machine learning methods. However, deep learning models exhibited reduced interpretability compared to machine learning approaches, which explicitly utilized handcrafted features for prediction. Guided class activation mapping (Guided CAM) visualizations confirmed that deep learning models relied primarily on the lower molar and premolar regions for age estimation, aligning with known dental aging indicators. Despite their lower interpretability, deep learning methods offered the advantage of automatic feature extraction, reducing the need for manual preprocessing. Overall, this study underscores the adaptability of machine learning and deep learning techniques to diverse dental structures. While ensemble machine learning models demonstrated superior accuracy, deep learning approaches provided a more automated yet less interpretable solution. The results highlight the potential for integrating both methodologies to enhance the reliability and precision of dental age estimation in forensic applications.