AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment

TL;DR

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.

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.

Figures (5)

• Figure 1: Main entities of AIdentifyAGE judicial/forensic domain. This includes information related to a legal dental medical exam (Legal Dental Medical Exam Data) performed by a forensic expert on an undocumented individual. At the end of all legal procedures, also include information regarding the judicial report (Report Data), containing the age assessment conclusion. The non labeled arrows define rdfs:subClassOf property relations. The labeled ones correspond to specific object properties.
• Figure 2: Main entities of AIdentifyAGE manual daa domain. This includes information regarding Tooth development stage scoring (Tooth Stage). Given that a set of Tooth is scored, a set of Reference Study is applied to calculate statistical measures to produce a Dental Age Assessment conclusion. Data Reference Study and Coefficient Maturity Data contain the statistically significant information that allows the daa to be performed. The non labeled arrows define rdfs:subClassOf property relations. The labeled ones correspond to specific object properties.
• Figure 3: Main entities of AIdentifyAGE ai daa domain. This includes information regarding the use of machine-learning models (Model) to perform two types of daa: classification (ai Dental Age Threshold Assessment) and regression (ai Reg Dental Age Assessment). These models were configured using the hyper-parameterizations included in ModelCharacteristic, to perform inference (Inference Run) over a set of images present in Data Collection, producing multiple Model Output. The non labeled arrows define rdfs:subClassOf property relations. The labeled ones correspond to specific object properties.
• Figure 4: AIdentifyAGE creation process. Starting from literature revision, going through the taxonomy creation, annotation, and hierarchical classification following the OBI ontology, ending with the ontology (iterative) validation, producing the validated ontology.
• Figure 5: This illustrative SPARQL query demonstrates how the AIdentifyAGE ontology enables simultaneous retrieval of manual daa results and ai‑based assessment provenance within a unified semantic framework. It retrieves statistically outputs from a manual daa, namely mean estimated age, standard deviation, and age interval, together with information about the ai model task type used in an ai‑based assessment (classification or regression).