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Quantitative Assessment of Third Molar Extraction Difficulty and Nerve Injury Risk Using Artificial Intelligence and

Jihyeong Ko1, Sasi Sooksatra1, Seungmin Kim1

  • 1Department of Biomedical Engineering, Chonnam National University, Yeosu, 59626, Korea.

Annals of Biomedical Engineering
|April 12, 2026
PubMed
Summary
This summary is machine-generated.

This study developed an AI-powered system using panoramic X-rays to precisely assess third molar (3M) extraction difficulty and nerve damage risk. The new scoring system aids dentists in surgical planning for impacted teeth.

Keywords:
Artificial intelligenceDental panoramic radiographyImage processingInferior alveolar nerve injuryQuantitative assessmentThird molar extraction

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Third molar (3M) extraction complexity is influenced by tooth position and surrounding tissues.
  • Accurate assessment of 3M extraction difficulty is crucial for surgical planning and risk mitigation.

Purpose of the Study:

  • To develop a novel, precise scoring system for third molar (3M) extraction difficulty.
  • To leverage AI and image computational techniques on panoramic radiography for enhanced surgical assessment.

Main Methods:

  • Utilized AI deep learning models to detect and segment the mandibular canal (MC), inner area of alveolar bone (IAAB), and 3M from panoramic X-rays.
  • Developed algorithms to score 3M inclination, impaction depth, and proximity to the MC.
  • Summed scores to create a difficulty index ranging from very easy to very difficult.

Main Results:

  • High average precisions for 3M detection models (0.93-0.97).
  • Accurate performance in classifying 3M inclination (0.8544), impaction depth (0.9515), and M3M-MC proximity (0.8991).

Conclusions:

  • The AI-driven scoring model assists dentists in evaluating 3M extraction risks and planning surgeries.
  • Further research with diverse data and improved AI models is needed for clinical application.