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The use of artificial intelligence in predicting maximal intercuspal position: A feasibility study.

Jiamin Wu1, Ki Hin Yuen2, Yun Hong Lee1

  • 1Dental Materials Science, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR.

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Summary

This study shows artificial intelligence (AI) can accurately predict maxillomandibular relationships using teeth scans. The AI system achieved discrepancies under 1.3 mm and 1.5°, demonstrating its potential in dental applications.

Keywords:
Artificial intelligenceDeep learningDental occlusionJaw relation recordMachine learning

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Predicting maxillomandibular relationships is crucial in dentistry.
  • Insufficient occluding teeth pairs can complicate this prediction.
  • Artificial intelligence (AI) offers a potential solution for learning and predicting these relationships.

Purpose of the Study:

  • To assess the feasibility of a two-stage AI pipeline for predicting maxillomandibular relationships.
  • To utilize occlusal morphology of antagonistic teeth for AI-driven predictions.
  • To evaluate the accuracy of AI in determining teeth alignment.

Main Methods:

  • Trained a deep learning alignment network on 300 pairs of scanned maxillary and mandibular casts.
  • Validated the AI system on 25 unseen cast pairs in maximal intercuspal position (MIP).
  • Compared AI-predicted relationships with mounted casts using rotational and translational discrepancies.

Main Results:

  • AI-predicted maxillomandibular relationships showed mean rotational discrepancies of approximately 1.4° (x-axis), 1.3° (y-axis), and 0.7° (z-axis).
  • Mean translational discrepancies were around 0.2 mm (x-axis), 1.2 mm (y-axis), and -1.0 mm (z-axis).
  • The AI system demonstrated high accuracy in predicting teeth alignment.

Conclusions:

  • The AI-predicted maxillomandibular relationship closely matches actual relationships.
  • Discrepancies were minimal, averaging less than 1.3 mm and 1.5°.
  • This AI approach shows promise for accurate dental relationship prediction.