AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment

TL;DR

The paper introduces the AIdentifyAGE ontology, a standardized framework designed to improve transparency, interoperability, and reproducibility in forensic dental age assessment, integrating manual and AI methods within medico-legal workflows.

Contribution

It presents a domain-specific ontology that models the entire forensic dental age assessment process, enhancing consistency and traceability across clinical, forensic, and legal systems.

Findings

Developed a comprehensive ontology integrating forensic workflows and AI methods.

Ensures interoperability with existing biomedical and machine learning ontologies.

Supports transparency and explainability in forensic dental age assessments.

Abstract

Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI- based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and reported outcomes. It models the complete 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. It is being developed together with domain experts, and it builds on upper and established biomedical, dental, and machine learning ontologies, ensuring interoperability, extensibility, and compliance with FAIR principles. The AIdentifyAGE ontology is a fundamental step to enhance consistency, transparency, and explainability, establishing a robust foundation for ontology-driven decision support systems in medico-legal and judicial contexts.