2/20/2026 | USA | technology | ✓ Verified - arxiv.org

AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment

#Forensic dental age assessment #AIdentifyAGE ontology #AI in forensics #Judicial decision-making #Medical standardization #Data interoperability #FAIR principles

📌 Key Takeaways

AIdentifyAGE ontology standardizes forensic dental age assessment processes
The framework addresses challenges in judicial decision-making for undocumented individuals and minors
It integrates both manual and AI-assisted assessment methods
The ontology ensures traceable linkage between observations, methods, and outcomes
Development involved collaboration with domain experts and builds on existing ontologies

📖 Full Retelling

A team of researchers led by Renato Marcelo and including Ana Rodrigues, Cristiana Palmela Pereira, António Figueiras, Rui Santos, José Rui Figueira, Alexandre P Francisco, and Cátia Vaz has developed the AIdentifyAGE ontology to standardize forensic dental age assessment processes, addressing critical challenges in judicial decision-making for undocumented individuals and unaccompanied minors. The research, submitted to arXiv on January 28, 2026, introduces a domain-specific framework that aims to improve consistency, transparency, and explainability in age determination methods that are crucial for legal determinations. Forensic dental age assessment is widely recognized as one of the most reliable biological approaches for determining the age of adolescents and young adults, particularly in legal contexts where age can determine access to protection, healthcare, and appropriate judicial procedures. However, current practices face significant challenges including methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations are further complicated by the increasing adoption of AI-based methods, which can introduce additional complexity to the decision-making process without proper standardization. The AIdentifyAGE ontology provides a standardized, semantically coherent framework that encompasses both manual and AI-assisted forensic dental age assessment workflows, enabling traceable linkage between observations, methods, reference data, and reported outcomes while modeling the complete medico-legal workflow.

🏷️ Themes

Forensic Science, Artificial Intelligence, Medical Decision Support

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Deep Analysis

Why It Matters

The ontology standardizes forensic dental age assessment, improving transparency and reproducibility across legal and medical contexts. It supports AI integration while ensuring compliance with FAIR principles.

Context & Background

Forensic age assessment is critical for legal decisions involving undocumented minors.
Current methods lack interoperability and consistency.
An ontology can unify data and enable AI-driven decision support.

What Happens Next

The ontology will be integrated into existing forensic and clinical systems, facilitating automated reporting and cross‑institution data sharing. Future work may expand it to other biological age markers and support real‑time decision tools.

Frequently Asked Questions

What is the main purpose of the AIdentifyAGE ontology?

To provide a standardized, semantically coherent framework for forensic dental age assessment workflows, linking observations, methods, and outcomes.

How does it improve AI-based age estimation?

By offering a structured data model that enables traceable linkage between raw imaging, reference studies, and AI predictions, enhancing explainability.

Is the ontology publicly available?

Yes, it is released under open licenses and can be accessed via the arXiv DOI link provided in the paper.

What are the next steps for adoption?

Stakeholders will collaborate to integrate the ontology into forensic software, conduct validation studies, and extend it to other age assessment domains.

Original Source

              --> Computer Science > Artificial Intelligence arXiv:2602.16714 [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 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 st...
            

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Source

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