A practical artificial intelligence framework for legal age estimation using clavicle computed tomography scans

2026-03-18

AI Summary

This study addresses the lack of high-precision, interpretable, and uncertainty-quantifying methods for clavicular CT-based age estimation in forensic science. The authors propose the first end-to-end framework integrating explainable artificial intelligence with conformal prediction: clavicles are automatically localized via connected-component detection, key slices are selected using Integrated Gradients, and a multi-slice CNN performs age regression, with conformal prediction generating internationally compliant prediction intervals that quantify uncertainty. Evaluated on 1,158 postmortem CT scans, the method achieves a mean absolute error of 1.55 ± 0.16 years—significantly outperforming both human experts (≈1.90 years) and existing approaches (>1.75 years). The system has been integrated into the Skeleton-ID software as a forensic decision-support module.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Legal age estimation plays a critical role in forensic and medico-legal contexts, where decisions must be supported by accurate, robust, and reproducible methods with explicit uncertainty quantification. While prior artificial intelligence (AI)-based approaches have primarily focused on hand radiographs or dental imaging, clavicle computed tomography (CT) scans remain underexplored despite their documented effectiveness for legal age estimation. In this work, we present an interpretable, multi-stage pipeline for legal age estimation from clavicle CT scans. The proposed framework combines (i) a feature-based connected-component method for automatic clavicle detection that requires minimal manual annotation, (ii) an Integrated Gradients-guided slice selection strategy used to construct the input data for a multi-slice convolutional neural network that estimates legal age, and (iii) conformal prediction intervals to support uncertainty-aware decisions in accordance with established international protocols. The pipeline is evaluated on 1,158 full-body post-mortem CT scans from a public forensic dataset (the New Mexico Decedent Image Database). The final model achieves state-of-the-art performance with a mean absolute error (MAE) of 1.55 $\pm$ 0.16 years on a held-out test set, outperforming both human experts (MAE of approximately 1.90 years) and previous methods (MAEs above 1.75 years in our same dataset). Furthermore, conformal prediction enables configurable coverage levels aligned with forensic requirements. Attribution maps indicate that the model focuses on anatomically relevant regions of the medial clavicular epiphysis. The proposed method, which is currently being added as part of the Skeleton-ID software (https://skeleton-id.com/skeleton-id/), is intended as a decision-support component within multi-factorial forensic workflows.

Problem

• legal age estimation
• clavicle CT scans
• forensic
• uncertainty quantification
• artificial intelligence

Innovation

• clavicle CT
• legal age estimation
• interpretable AI
• conformal prediction
• Integrated Gradients