Exploring the Advancements of Forensic Odontology: A Review

Amirmohammad  Moradpour; Samareh  Mortazavi; Iman  Shiezadeh

doi:10.18502/jcr.v13i1.21473

Amirmohammad Moradpour Student Research Committee, Faculty of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran.
Samareh Mortazavi Department of Oral and Maxillofacial Radiology, School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran.
Iman Shiezadeh Student Research Committee, Faculty of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran.

DOI: https://doi.org/10.18502/jcr.v13i1.21473

Keywords: Artificial intelligence; Dental identification; Forensic dentistry; Forensic odontology.

Abstract

Introduction: Forensic dentistry is essential for human identification, but traditional manual techniques are often time-intensive and prone to subjectivity. The integration of artificial intelligence (AI) presents a transformative opportunity to enhance accuracy and efficiency across various forensic odontology applications. This systematic review consolidates and analyzes the existing literature on AI applications in forensic dentistry.

Materials and Methods: A comprehensive literature search was conducted in the PubMed and Scopus databases following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies published up to August 2025 were considered for inclusion.

Results: A total of 116 studies were analyzed. The primary applications included age estimation (54 studies), gender estimation (33 studies), and human/dental identification (26 studies). Bite mark analysis (1 study) and facial reconstruction (2 studies) were identified as underexplored areas. Convolutional neural networks (CNNs) were the most frequently employed AI algorithm, utilized in 64% (74/116) of studies, while orthopantomograms (OPGs) were the predominant imaging modality in 72% (84/116) of investigations. Quantitative results demonstrated significant promise: age estimation models achieved a mean absolute error (MAE) as low as 0.0079 years and a peak classification accuracy of 99.98%; gender estimation models attained accuracies between 68% and 98%; and human identification models reached up to 100% accuracy in optimal conditions, though sensitivity dropped to 69% in complex postmortem cases. Emerging techniques, such as microbiome analysis and generative adversarial networks (GANs), were noted as innovative future directions.

Conclusion: AI demonstrates significant potential to improve accuracy and reduce processing time in core forensic dentistry tasks, particularly age and gender estimation. However, challenges related to data privacy, algorithmic bias, and legal admissibility persist. Future research should prioritize the development of explainable AI models, standardized and diverse datasets, and robust regulatory frameworks to ensure ethical and trustworthy integration into forensic practice.