• Computer Science > Artificial Intelligence [Submitted on 28 Jan 2026] Title:AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment 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 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 es

Computer Science > Artificial Intelligence [Submitted on 28 Jan 2026] Title:AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment 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.

Article Summaries:

  • A new ontology, AIdentifyAGE, has been released to standardise forensic dental age assessment. Designed with domain experts, it maps the entire medico‑legal workflow-from individual data and judicial context to dental examination, radiographic imaging, statistical reference studies, and AI‑based estimation methods. By building on established biomedical, dental, and machine‑learning ontologies, it promotes interoperability and compliance with FAIR principles. The framework aims to improve transparency, reproducibility, and explainability of age‑assessment decisions, supporting both manual and AI‑assisted workflows in forensic and judicial settings.
  • A new ontology, AIdentifyAGE, has been released to standardise forensic dental age assessment. Designed with domain experts, it maps the entire medico‑legal workflow-from individual data and judicial context to dental examination methods, radiographic imaging, reference studies and AI‑based estimations. By linking observations, methods, and outcomes in a semantically coherent framework, the ontology addresses current heterogeneity and limited interoperability in dental age assessment. Built on established biomedical, dental and machine‑learning ontologies, it supports FAIR principles, enabling transparent, reproducible, and explainable decision‑support systems for forensic and judicial use.

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