AI-Powered Dental Forensics in Transforming Age Estimation Techniques: A Narrative Review

Artificial Intelligence (AI) has opened new avenues for researchers all over the world with its rapid results, accuracy and efficiency. This paper specifically aims to identify the role of AI in forensic odontology by assisting odontologists in age estimation. It discusses the evolution from conventional methods to fast and efficient AI models, including Convolutional Neural Networks (CNN), Deep Learning (DL) and Machine Learning (ML) and compares the efficiency of each to conclude which AI model yields the best results for researchers. The paper also highlights the role of the CNN network in dental age estimation using Orthopantomogram (OPG), Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) datasets. It aims to help researchers incorporate global advancements into their studies, with a focus on applications for the Indian population. OPG is widely used in routine dental examinations due to its efficiency and accessibility; however, it has limitations, such as image distortion and the lack of 3D visualisation. AI models, particularly CNNs, have improved the accuracy of age estimation from OPG images. Magnetic Resonance Imaging (MRI), though less commonly utilised, is beneficial for non invasive and precise age estimation in younger individuals because it provides clear images of pulp cavities. Cone Beam Computed Tomography (CBCT) offers superior 3D imaging, allowing for more accurate assessments of pulp-to-tooth ratios and root development, although it involves higher radiation exposure. This review highlights how combining AI with different imaging techniques can enhance accuracy, reduce human error and address population-specific variations in forensic age estimation.