Applying artificial intelligence to determination of legal age of majority from radiographic data

J. Murray (Lead / Corresponding author), D. Heng, A. Lygate, L. Porto, A. Abade, S. Manica, A. Franco

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
71 Downloads (Pure)

Abstract

Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into 'under-18' and 'over-18' age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.

Original languageEnglish
Article number100723
Number of pages12
JournalMorphologie
Volume108
Issue number360
Early online date31 Oct 2023
DOIs
Publication statusPublished - Mar 2024

Keywords

  • Forensic dentistry
  • Dental age
  • estimation
  • Panoramic
  • radiographs
  • Artifical intelligence
  • Deep learning
  • Machine vision
  • Convolutional neural network
  • Dental age estimation
  • Panoramic radiographs
  • Artificial intelligence

ASJC Scopus subject areas

  • Anatomy

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