Charting Age: A New Knowledge System for Forensic Dentistry

Author: Denis Avetisyan

Researchers have developed a comprehensive framework to standardize and improve the accuracy of dental age estimation, a critical process in forensic investigations.

The system models the essential entities within judicial and forensic age assessment, encompassing data from legal dental and medical examinations performed on undocumented individuals-detailed as [latex]Legal\,Dental\,Medical\,Exam\,Data[/latex]-and culminating in a comprehensive [latex]Report\,Data[/latex] that formally concludes the age determination process, with relationships defined through [latex]rdfs:subClassOf[/latex] and specific object properties.

AIdentifyAGE ontology formally represents the dental age assessment workflow, integrating manual and AI-driven methods for enhanced transparency and interoperability.

Forensic dental age assessment, while crucial for legal determinations involving vulnerable populations, suffers from methodological inconsistencies and limited data interoperability. The development of the AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment addresses this challenge by providing a standardized, semantically coherent framework integrating manual techniques, AI-driven methods, and the broader medico-legal context. This ontology models the complete workflow, enabling traceable linkages between observations, methods, and outcomes, thereby enhancing transparency and reproducibility. Will this formalized knowledge representation pave the way for robust, ontology-driven decision support systems within forensic and judicial domains?

The Imperative of Precise Age Determination

Establishing a person’s age is often a critical first step in forensic investigations, becoming especially vital when identifying remains or individuals lacking documentation. Traditional methods, such as fingerprint analysis or DNA profiling, are not always definitive in determining age, leaving investigators to rely on biological indicators. In scenarios involving mass disasters, unidentified migrants, or the long-term missing, age estimation provides a crucial narrowing of the search parameters and can significantly aid in establishing a potential identity. The accuracy of this initial assessment can dramatically influence the course of an investigation, impacting everything from locating family members to building a legally sound case – highlighting the necessity for robust and reliable age determination techniques when conventional identification fails.

Despite decades of use in forensic investigations, manual dental age assessment remains inherently susceptible to human interpretation, introducing a significant degree of subjectivity. This reliance on visual estimations of dental development – examining attributes like tooth formation and eruption – means different odontologists can arrive at varying age estimations for the same individual. Such inter-observer variability doesn’t simply represent minor discrepancies; it can lead to substantial differences in estimated age ranges, potentially impacting legal proceedings. Because the accuracy of age estimation directly influences the judicial process, these inconsistencies raise concerns about the reliability of dental evidence presented in court, and can become a focal point for legal challenges questioning the validity of the findings and the fairness of the outcome.

The escalating intricacy of modern forensic investigations is driving a critical need for advancements in age estimation techniques. Contemporary cases frequently involve fragmented remains, mass disasters, or individuals with limited identifying documentation, rendering traditional methods insufficient. Furthermore, the rise in international human trafficking and undocumented migration presents unique challenges, demanding a higher degree of accuracy than current subjective assessments can consistently provide. Consequently, researchers are actively pursuing objective, reproducible methods – encompassing advancements in dental maturity assessments, skeletal development analysis utilizing imaging technologies, and increasingly, the exploration of biochemical markers like DNA methylation – to establish reliable age ranges and contribute to positive identification in complex forensic scenarios.

AIdentifyAGE determines dental age by scoring tooth development stage using reference studies and maturity data, leveraging [latex]rdfs:subClassOf[/latex] relationships to define entity hierarchies and specific object properties for assessment.

AI-Driven Precision: A Paradigm Shift in Forensic Odontology

AI-based dental age assessment presents a viable alternative to traditional methods, which are often subjective and require specialized training. This technology utilizes machine learning algorithms, specifically designed to automate the process of age estimation from dental radiographs. By applying these algorithms, the system standardizes age assessment, reducing inter-observer variability and improving the reproducibility of results. This automation increases efficiency in contexts requiring age determination, such as forensic investigations, unidentified remains analysis, and cases involving individuals without documented identification.

Convolutional Neural Networks (CNNs) achieve high accuracy in dental age assessment by automatically extracting and analyzing features from radiographic images, specifically orthopantomographs. These networks are trained on large datasets of images with known ages, enabling them to identify subtle, often imperceptible, indicators of biological maturity – such as root development, trabecular bone structure, and the degree of dental attrition. The CNN architecture utilizes multiple layers of convolutional filters to progressively learn hierarchical representations of these features, ultimately classifying the image with an associated age prediction and reported confidence interval. Accuracy rates, as demonstrated in peer-reviewed studies, frequently exceed those of traditional methods relying on manual assessment of developmental stages.

The AI-driven dental age assessment system functions by analyzing orthopantomography (OPG) images, a type of panoramic dental X-ray. The AI model, utilizing machine learning algorithms, processes these 2D radiographic images to identify specific dental development indicators and patterns associated with chronological age. These indicators include the degree of root formation, the number and position of developing teeth, and the presence of fully erupted teeth. By quantifying these features, the system generates an objective age prediction, expressed as a numerical estimate, which can be used as supplemental data in age estimation procedures. The system’s reliability is dependent on the quality and standardization of the input OPG images, as well as the size and diversity of the training dataset used to develop the AI model.

AIdentifyAGEAIDAAdomain leverages machine-learning models, configured by [latex]ModelCharacteristic[/latex], to perform dental age assessment via classification ([latex]AIDental Age Threshold Assessment[/latex]) and regression ([latex]AIReg Dental Age Assessment[/latex]) on images from [latex]Data Collection[/latex], generating [latex]Model Output[/latex] through [latex]Inference Run[/latex] and defined by subclass and object property relationships.

AIdentifyAGE: Formalizing Forensic Dental Assessment

The AIdentifyAGE Ontology structures the forensic dental age assessment process through a formalized, machine-readable framework consisting of 1448 distinct classes. These classes represent entities and relationships encompassing all stages of assessment, from initial data acquisition – including patient demographics and clinical findings – to the application of AI-driven predictive models and the generation of final age estimations. This granular level of detail allows for precise modeling of complex workflows, facilitating data standardization, interoperability, and the transparent tracking of evidence used in legal contexts. The ontology’s scope includes representation of dental features, radiographic analyses, population-specific data, and associated metadata, enabling a complete and auditable record of the assessment process.

AIdentifyAGE establishes a verifiable audit trail by systematically combining data from multiple sources. Judicial context, including legal requirements and admissibility standards, is incorporated alongside detailed clinical observations recorded during dental examinations. These data are then linked to the outputs of AI-driven age prediction models. This integration allows for a complete reconstruction of the reasoning process behind any age estimation, facilitating review by legal professionals and ensuring accountability for the conclusions reached. The system documents not only the final prediction but also the specific evidence and algorithmic steps that contributed to it, thereby promoting transparency throughout the forensic workflow.

The AIdentifyAGE ontology utilizes the SPARQL query language to facilitate data interrogation and interpretation within forensic contexts. SPARQL, a standardized query language for RDF data, allows for precise and efficient retrieval of specific information related to dental age assessment, including clinical observations, AI prediction results, and relevant judicial context. This capability is crucial for generating reports and providing evidence that is directly traceable and interpretable within legal proceedings. The structured nature of SPARQL queries ensures that data retrieval is reproducible and auditable, supporting the transparency and accountability requirements of forensic science and legal standards. Furthermore, SPARQL’s ability to navigate relationships between different data points within the ontology enables complex inquiries and the reconstruction of the reasoning behind age estimations.

The AIdentifyAGE ontology is developed through an iterative process of literature review, taxonomy and annotation creation, hierarchical classification based on the OBI ontology, and validation to ensure a robust and reliable resource.

Standardization and Validation: Ensuring Robustness in Forensic Practice

The AIdentifyAGE system deliberately integrates with long-standing practices in forensic odontology, most notably building upon the widely-used Demirjian Method for age estimation from dental development. To ensure data uniformity and facilitate interoperability, the system employs standardized dental notation systems, specifically the Universal Numbering System (UNS) and the Fédération Dentaire Internationale (FDI) Notation. This adherence to established protocols is critical; it allows for seamless integration with existing forensic databases and workflows, while also promoting consistency and comparability of age assessments across different studies and practitioners. By grounding its AI-driven analysis in these proven methodologies and standardized notations, AIdentifyAGE aims to provide reliable and defensible age estimations within the legal and humanitarian contexts where accurate assessment is paramount.

AIdentifyAGE directly links artificial intelligence predictions to recognized stages of tooth development, a cornerstone of forensic dental age estimation. This approach moves beyond simply identifying features to explicitly modeling the biological processes underlying dental maturation – stages like crown completion, root initiation, and apex closure. By incorporating these established developmental benchmarks, the ontology ensures that AI-driven age assessments are not merely correlational, but are anchored in verifiable biological realities. This grounding in established markers enhances the reliability and interpretability of predictions, offering a transparent link between AI outputs and the fundamental principles of dental age estimation used by forensic odontologists.

The AIdentifyAGE Ontology functions as a highly granular knowledge base for forensic dental age estimation, meticulously detailing the complex relationships between observable dental traits and chronological age. Its architecture comprises 97 object properties – defining the entities involved, such as specific teeth, developmental stages, or imaging modalities – and 56 data properties, which characterize these entities with attributes like crown diameter, root length, or pulp chamber size. This extensive network of properties doesn’t simply catalogue information; it structures the entire assessment process, enabling AI algorithms to interpret dental features not as isolated data points, but as interconnected elements within a biologically meaningful framework. The result is a robust, standardized, and machine-readable representation of dental age assessment, fostering greater accuracy, transparency, and reliability in forensic investigations.

The Future of Forensic Dentistry: A System Built for Evolution

AIdentifyAGE represents a significant advancement in forensic dental age estimation through its foundation on established ontologies – the Ontology for Biomedical Investigations and the ML-Schema Ontology. This architectural choice isn’t merely technical; it establishes a highly scalable and adaptable platform capable of incorporating new artificial intelligence models and diverse datasets as they emerge. Unlike traditional, static methods, AIdentifyAGE’s ontological framework allows for continuous refinement of age assessment accuracy and efficiency. The system isn’t limited by pre-defined parameters; instead, it leverages interconnected knowledge to dynamically adjust to variations in dental development and population characteristics, promising a more robust and future-proof approach to forensic identification.

The AIdentifyAGE framework is designed not as a static solution, but as a continuously evolving system for dental age estimation. Its architecture prioritizes adaptability, allowing for the seamless incorporation of emerging artificial intelligence models and diverse datasets. This capacity for expansion is crucial, as advancements in machine learning and the availability of new imaging techniques-such as micro-CT scans and improved radiographic analysis-constantly refine the potential for accurate age prediction. By readily accepting these innovations, the framework ensures that forensic dental age assessment remains at the forefront of scientific progress, promising increasingly precise and efficient results in legal and humanitarian contexts. This dynamic approach moves beyond traditional methods, creating a resilient system capable of responding to the ever-changing landscape of digital forensics.

AIdentifyAGE establishes a comprehensive foundation for modern forensic odontology through a meticulously constructed knowledge base. The system leverages 316 semantic annotations, strategically applied across 70 distinct term classes-encompassing dental features and aging indicators-and linked by 28 object properties defining relationships between these terms. Furthermore, 58 data properties provide quantifiable characteristics, allowing for nuanced analysis and integration with artificial intelligence models. This richly interconnected network doesn’t simply catalog dental information; it creates a dynamic, machine-readable environment where complex relationships between dental development and age can be explored, validated, and continually refined, ultimately enhancing the precision and reliability of forensic age estimation.

The development of AIdentifyAGE, as detailed in the article, embodies a commitment to formalizing knowledge – a pursuit mirroring the analytical spirit of Ada Lovelace. She observed that “The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform.” This resonates deeply with the ontology’s structure; it doesn’t create age assessments, but meticulously defines the logical steps and data relationships required to represent existing methods, both manual and AI-driven. By explicitly articulating these processes, AIdentifyAGE moves beyond simply achieving functional results and instead focuses on provable correctness – a hallmark of robust knowledge representation and a principle Lovelace would undoubtedly appreciate. The focus on interoperability within the ontology demonstrates a practical application of theoretical rigor, enabling a seamless connection between diverse data sources and analytical techniques.

What Lies Ahead?

The formalization of dental age assessment, as attempted by AIdentifyAGE, is a necessary, though hardly sufficient, step. The ontology’s strength resides in its attempt to map a traditionally subjective process onto a logically consistent framework. However, the devil, predictably, remains in the details – specifically, the inherent ambiguity of biological markers. A perfectly represented workflow is useless if the underlying data – the tooth development itself – is not wholly deterministic. One cannot legislate away the variance of human biology with a well-structured taxonomy.

Future work must address the critical issue of reproducibility. If an AI, guided by this ontology, arrives at differing age estimations given identical input data-even within acceptable error margins-the entire exercise is compromised. Forensic science demands demonstrable, unwavering results, not probabilistic approximations. The focus should shift from merely representing the process to rigorously quantifying uncertainty at each stage, and propagating that uncertainty through the entire assessment.

Ultimately, the true test of such an ontology will be its utility in challenging cases-those where expert opinions diverge. Will AIdentifyAGE offer a means of resolving disputes through logical deduction, or will it merely provide a more elaborate framework for disagreeing? The answer, one suspects, will reveal more about the limits of formalization than about the science of age estimation itself.

Original article: https://arxiv.org/pdf/2602.16714.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/