Modernizing Forensic Anthropology: A Data-driven Pipeline for Human Identification and Profiling.
Authors
Affiliations (10)
Affiliations (10)
- Fundamental Dental and Medical Sciences, Kulliyyah of Dentistry, International Islamic University Malaysia, Malaysia.
- Institute of Advanced Dental Sciences and Research, Lahore, Pakistan.
- Department of Restorative Dental Science, College of Dentistry and Dental Hospital, Taibah University, Medina, Saudi Arabia.
- University Dental Hospital, Taibah University, Medina, Saudi Arabia.
- Center for Sustainability and Climate and Center of Excellence in Cybersecurity, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia.
- Department of Oral Biology, CMH Lahore Medical College and Institute of Dentistry, Lahore, Pakistan.
- Department of Clinical Sciences, College of Dentistry, Ajman University, Ajman, United Arab Emirates.
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.
- School of Dentistry, Jordan University, Amman, Jordan.
- Department of Forensic Medicine, Shifa College of Medicine, Islamabad, Pakistan.
Abstract
Forensic odontology has traditionally relied on dental morphology and odontometric measurements for identification and profiling purposes. Innovations in imaging technologies (high-resolution two-dimensional [2D] radiography, cone-beam computed tomography [CBCT], and intraoral three-dimensional [3D] scanning), geometric morphometrics analysis (GMA), and artificial intelligence (AI) have revolutionized the collection, analysis, and interpretation of dental data. Relevant literature was identified through searches in PubMed, Scopus, and Web of Science using the keywords forensic odontology, CBCT, GMA, AI segmentation, and human identification, focusing on English-language studies published between 2010 and 2025. This narrative review consolidates the existing evidence regarding (1) the enhancement of data acquisition and comparability through 2D and 3D imaging; (2) the quantification of dental sexual dimorphism by GMA and its application in machine learning (ML) classifiers; (3) recent advancements in sex-prediction models derived from tooth metrics and 3D shape data; and (4) the facilitation of dental model creation and identification workflows through AI-driven segmentation. The discussion encompasses practical benefits, existing limitations, validation requirements, and prospective directions for the adoption of this technique in forensic applications.