Current Advances in Age and Sex Estimation Using Artificial Intelligence in Forensic Odontology

Abstract

Forensic odontology plays a crucial role in human identification, particularly in estimating age and sex in individuals without official identification documents. Although traditional methods are widely used, they have significant limitations such as population variability, observer-related errors, and low reproducibility. In recent years, artificial intelligence (AI) and machine learning approaches have provided more objective, rapid, and reproducible alternatives. In particular, convolutional neural network-based algorithms have reduced mean absolute error in age estimation and achieved accuracy rates exceeding 90% in sex classification by automatically extracting discriminative features from panoramic radiographs and cone-beam computed tomography data. National and international studies support the reliability of these technologies, especially in pediatric and adolescent populations where traditional methods are less precise. However, dataset biases, population-specific variability, limited generalizability across ethnic groups, and the non-transparent nature of deep learning models continue to pose challenges for interpretability and legal admissibility. Therefore, ethical and regulatory frameworks emphasizing transparency, data protection, and explainable-AI (XAI) principles are essential for the responsible implementation of these technologies. Future progress will depend on large-scale, multicenter validation studies and the integration of multimodal datasets combining radiographic, morphological, and demographic information. The adoption of XAI frameworks is expected to enhance transparency, accountability, and forensic reliability, thereby enabling. AI-based systems to become scientifically robust and legally valid tools in forensic odontology.