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Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M.

Romain Bui1,2, Régis Iozzino1,2, Raphaël Richert1,2

  • 1Pôle d'Odontologie, Hospices Civils de Lyon, 69008 Lyon, France.

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|March 11, 2023
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Summary

This study explored automating dental age estimation using the third molar maturity index (I3M). A deep learning model combined with topological data analysis achieved 95% accuracy, supporting expert decisions.

Keywords:
age estimationartificial intelligencedeep learningdentistrymachine learningneural networktopological analysis

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Area of Science:

  • Forensic Anthropology
  • Radiology
  • Computer Science

Background:

  • Dental age estimation is crucial in forensics.
  • The third molar maturity index (I3M) is a common method.
  • Expert determination of I3M can be subjective.

Purpose of the Study:

  • To assess the feasibility of an automated I3M decision-making tool.
  • To support expert judgment in dental age estimation.
  • To leverage deep learning and topological data analysis for I3M scoring.

Main Methods:

  • Compared Mask R-CNN and U-Net for mandibular radiograph segmentation.
  • Applied topological data analysis (TDA) and TDA with deep learning (TDA-DL) for I3M calculation.
  • Validated results against dental forensic expert assessments.

Main Results:

  • U-Net achieved higher segmentation accuracy (mIoU 91.2%) than Mask R-CNN (83.8%).
  • Automated I3M scores showed low mean absolute errors (0.04 ± 0.03 for TDA, 0.06 ± 0.04 for TDA-DL).
  • High Pearson correlation coefficients (0.93 with TDA, 0.89 with TDA-DL) were observed between automated and expert scores.

Conclusions:

  • A combined deep learning and topological approach shows potential for automating I3M assessment.
  • This automated solution achieved 95% accuracy compared to expert evaluation.
  • The developed tool can aid in objective and efficient dental age estimation.