AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment

2026-02-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, extended

Summary

The AIdentifyAGE ontology provides a standardized, semantically coherent framework for forensic dental age assessment (DAA), addressing challenges like methodological heterogeneity and fragmented data in medico-legal decision-making. Developed in collaboration with domain experts, this ontology integrates judicial context, individual information, forensic examination data, dental developmental assessment methods, radiographic imaging (specifically orthopantomography or OPG), statistical reference studies, and AI-based estimation methods. It is structured into three domains: Judicial/Forensic, Manual DAA, and AI-based DAA, ensuring traceability between observations, methods, reference data, and reported outcomes. The ontology builds upon established biomedical, dental, and machine learning ontologies, adhering to FAIR principles, and is publicly available on BioPortal and GitHub, offering a robust foundation for ontology-driven decision support systems.

Key takeaway

For AI Scientists developing or deploying age assessment models in forensic contexts, AIdentifyAGE offers a critical semantic framework. Your work can achieve greater transparency and legal defensibility by aligning with this ontology, ensuring that AI-driven age estimations are traceable, interpretable, and integrated with established medico-legal workflows. This standardization is essential for robust decision support systems and mitigating ethical and regulatory risks associated with algorithmic decision-making in sensitive judicial cases.

Key insights

AIdentifyAGE ontology standardizes forensic dental age assessment, integrating manual and AI methods for transparent medico-legal decision support.

Principles

• Standardization enhances transparency and reproducibility.
• Ontologies enable structured knowledge representation and interoperability.
• Traceability is crucial for algorithmic decision-making in legal contexts.

Method

The AIdentifyAGE ontology development involved knowledge-base creation using OBI as an upper framework, semantic enrichment of terms, linking to external ontologies, and multi-layer validation including logical consistency checks and functional adequacy assessment via SPARQL queries.

In practice

• Use AIdentifyAGE for consistent DAA data interpretation.
• Integrate AI model outputs with manual methods via the ontology.
• Query the ontology for traceable medico-legal conclusions.

Topics

• AIdentifyAGE Ontology
• Forensic Dental Age Assessment
• AI-based Age Estimation
• Medico-legal Decision Support
• Semantic Interoperability

Code references

• AIdentifyAGE/ontology

Best for: AI Scientist, Research Scientist, AI Engineer, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.