Age Assessment in Forensics: The AIdentifyAGE Ontology is Born

Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors. In these contexts, legal age thresholds determine access to protection, healthcare, and judicial procedures.

Dental age assessment is widely recognized as one of the most reliable biological approaches for age assessment in adolescents and young adults. However, current practices are often hampered by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations compromise transparency and reproducibility, a problem amplified by the increasing adoption of AI-based methods.

To address these challenges, the AIdentifyAGE ontology, specific to the age assessment domain, has been developed. It provides a standardized and semantically coherent framework, encompassing both manual and AI-assisted workflows. The ontology enables traceable linkage between observations, methods, reference data, and reported outcomes. It models the entire medico-legal workflow, integrating judicial context, individual-level information, forensic examination data, dental developmental assessment methods, radiographic imaging, statistical reference studies, and AI-based estimation methods.

The AIdentifyAGE ontology is being developed in collaboration with domain experts and builds on established biomedical, dental, and machine learning ontologies, ensuring interoperability, extensibility, and compliance with FAIR principles. It is a fundamental step to enhance consistency, transparency, and explainability, establishing a solid foundation for ontology-driven decision support systems in medico-legal and judicial contexts.