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Dental microfracture detection using wavelet features and machine learning.

Jared Vicory1, Ramraj Chandradevan1, Pablo Hernandez-Cerdan1

  • 1Kitware Inc, Carrboro NC 27510, USA.

Proceedings of Spie--The International Society for Optical Engineering
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PubMed
Summary
This summary is machine-generated.

A new algorithm uses high-resolution cone beam computed tomography (CBCT) scans and machine learning to detect microfractures in teeth. This advanced analysis offers a novel way to quantify tooth structural breakdown for earlier detection and better treatment outcomes.

Keywords:
Cracked teethDeep learningIsotropic wavelets

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Area of Science:

  • Dental diagnostics
  • Medical imaging analysis
  • Machine learning applications

Background:

  • Tooth microfractures are a significant cause of tooth loss.
  • Early detection of cracks is crucial for effective treatment and tooth retention.
  • Conventional cone beam computed tomography (CBCT) has limited success in detecting microfractures.

Purpose of the Study:

  • To develop and evaluate an algorithm for detecting microfractures in teeth.
  • To improve the accuracy of microfracture detection using advanced imaging and machine learning.
  • To provide a novel method for quantifying tooth structural breakdown.

Main Methods:

  • Simulated microfractures in extracted human teeth.
  • Acquired high-resolution (hr) CBCT and microCT scans.
  • Applied wavelet pyramid construction and a U-Net deep learning architecture for crack localization.
  • Quantified crack likelihood using the ratio of high-probability voxels to total tooth volume.

Main Results:

  • Fractured teeth exhibited a higher number of high-probability voxels compared to control teeth in both microCT and hr-CBCT scans.
  • The U-Net architecture successfully localized crack orientation and extent.
  • Generated slice-wise probability maps indicating the presence of microfractures.

Conclusions:

  • The proposed analytical framework offers a novel approach to quantify tooth structural breakdown.
  • Early detection of microfractures is achievable with the developed algorithm.
  • Future work includes expanding to 3D volumes and clinical validation for improved dental treatment.