Development of artificial intelligence-based algorithms for the process of human identification through dental evidence

• Published in: International journal of legal medicine Vol. 139; no. 4; pp. 1835 - 1850
• Main Authors: Palmela Pereira, Cristiana, Carvalho, Raquel, Augusto, Diana, Almeida, Tomás, Francisco, Alexandre P., Salvado e Silva, Francisco, Santos, Rui
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2025; Springer Nature B.V
• Subjects: Adolescent; Adult; Age; Age Determination by Teeth; Algorithms; Artificial Intelligence; Artificial neural networks; Cadavers; Classification; Female; Forensic computing; Forensic Dentistry - methods; Forensic Medicine; Forensic odontology; Humans; Identification; Image analysis; Male; Medical Law; Medicine; Medicine & Public Health; Neural Networks, Computer; Odontology; Original Article; Performance evaluation; Radiography, Panoramic; Young Adult; Deep learning; Orthopantomography; Human identification; Forensic odontology; Artificial intelligence
• ISSN: 0937-9827; 1437-1596; 1437-1596
• DOI: 10.1007/s00414-025-03453-x

Introduction Forensic Odontology plays a crucial role in medicolegal identification by comparing dental evidence in antemortem (AM) and postmortem (PM) dental records, including orthopantomograms (OPGs). Due to the complexity and time-consuming nature of this process, imaging analysis optimization is an urgent matter. Convolutional neural networks (CNN) are promising artificial intelligence (AI) structures in Forensic Odontology for their efficiency and detail in image analysis, making them a valuable tool in medicolegal identification. Therefore, this study focused on the development of a CNN algorithm capable of comparing AM and PM dental evidence in OPGs for the medicolegal identification of unknown cadavers. Materials and methods The present study included a total sample of 1235 OPGs from 1050 patients from the Stomatology Department of Unidade Local de Saúde Santa Maria, aged 16 to 30 years. Two algorithms were developed, one for age classification and another for positive identification, based on the pre-trained model VGG16, and performance was evaluated through predictive metrics and heatmaps. Results Both developed models achieved a final accuracy of 85%, reflecting high overall performance. The age classification model performed better at classifying OPGs from individuals aged between 16 and 23 years, while the positive identification model was significantly better at identifying pairs of OPGs from different individuals. Conclusions The developed AI model is useful in the medicolegal identification of unknown cadavers, with advantage in mass disaster victim identification context, by comparing AM and PM dental evidence in OPGs of individuals aged 16 to 30 years.