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[2602.16714] AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment
Summary
The AIdentifyAGE ontology aims to enhance forensic dental age assessment by providing a standardized framework for integrating clinical, forensic, and legal data, improving decision-making in judicial contexts.
Why It Matters
This ontology addresses critical challenges in forensic age assessment, such as methodological inconsistencies and data fragmentation. By standardizing processes and improving interoperability, it supports better legal outcomes for vulnerable populations, particularly undocumented minors.
Key Takeaways
- AIdentifyAGE ontology standardizes dental age assessment workflows.
- It enhances interoperability between clinical and legal information systems.
- The ontology supports both manual and AI-assisted assessment methods.
- It aims to improve transparency, consistency, and reproducibility in forensic practices.
- Developed with domain experts, it adheres to FAIR principles for data management.
Computer Science > Artificial Intelligence arXiv:2602.16714 (cs) [Submitted on 28 Jan 2026] Title:AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment Authors:Renato Marcelo, Ana Rodrigues, Cristiana Palmela Pereira, António Figueiras, Rui Santos, José Rui Figueira, Alexandre P Francisco, Cátia Vaz View a PDF of the paper titled AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment, by Renato Marcelo and 6 other authors View PDF HTML (experimental) 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 compl...