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Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites
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Automatic quantification framework to detect cracks in teeth.

Hina Shah1, Pablo Hernandez2, Francois Budin1

  • 1Kitware, Inc. 101 East Weaver Street, Carrboro, NC, USA 25710.

Proceedings of Spie--The International Society for Optical Engineering
|May 18, 2018
PubMed
Summary
This summary is machine-generated.

Early detection of cracked teeth, a leading cause of tooth loss, is crucial. This study introduces a novel method using advanced imaging and AI to accurately detect, quantify, and locate cracks, improving diagnosis and tooth preservation.

Keywords:
High-resolution Cone Beam Computed TomographyMachine learningTooth fracture detectionWavelet analysis

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cracked teeth are a significant cause of tooth loss in industrialized nations.
  • Current diagnostic tools and imaging methods like Cone Beam Computed Tomography (CBCT) lack sensitivity for early crack detection.
  • Delayed diagnosis of tooth cracks leads to further complications and potential tooth loss.

Purpose of the Study:

  • To introduce a novel, automated method for detecting, quantifying, and localizing cracks in teeth.
  • To improve the accuracy and sensitivity of dental crack diagnostics.
  • To enable earlier intervention for cracked teeth, potentially preventing tooth loss.

Main Methods:

  • Utilized high-resolution CBCT (hr-CBCT) scans of healthy and simulated cracked teeth.
  • Employed steerable wavelets and machine learning, specifically a bagged classifier of Support Vector Machines.
  • Trained crack detection on significant wavelet coefficients at various scales.

Main Results:

  • The novel method demonstrated high discriminative specificity and sensitivity in detecting simulated tooth cracks.
  • Successfully detected, quantified, and localized cracks in hr-CBCT scans.
  • The framework is designed to be automatic, reproducible, and open-source.

Conclusions:

  • The proposed method shows significant promise for accurate and early detection of cracked teeth.
  • This technique has the potential to improve the tracking and detection of various crack types.
  • Successful clinical validation could lead to improved patient outcomes and longer-lasting healthy teeth.