Open AccessDental microfracture detection using wavelet features and machine learning
Author(s) -
Jared Vicory,
Ramraj Chandradevan,
Pablo Hernandez-Cerdan,
Wei Angel Huang,
D.N. Fox,
Laith Abu Qdais,
Matthew McCormick,
André Mol,
R.G. Walter,
J. S. Marron,
Hassem Geha,
Asma Khan,
Beatriz Paniagua
Publication year - 2021
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2580744
Subject(s) - voxel , computer science , artificial intelligence , wavelet , cone beam computed tomography , feature extraction , wavelet transform , pattern recognition (psychology) , feature (linguistics) , computer vision , computed tomography , medicine , radiology , linguistics , philosophy
Microfractures (cracks) are the third most common cause of tooth loss in industrialized countries. If they are not detected early, they continue to progress until the tooth is lost. Cone beam computed tomography (CBCT) has been used to detect microfractures, but has had very limited success. We propose an algorithm to detect cracked teeth that pairs high resolution (hr) CBCT scans with advanced image analysis and machine learning. First, microfractures were simulated in extracted human teeth (n=22). hr-CBCT and microCT scans of the fractured and control teeth (n=14) were obtained. Wavelet pyramid construction was used to generate a phase image of the Fourier transformed scan which were fed to a U-Net deep learning architecture that localizes the orientation and extent of the crack which yields slice-wise probability maps that indicate the presence of microfractures. We then examine the ratio of high-probability voxels to total tooth volume to determine the likelihood of cracks per tooth. In microCT and hr-CBCT scans, fractured teeth have higher numbers of such voxels compared to control teeth. The proposed analytical framework provides a novel way to quantify the structural breakdown of teeth, that was not possible before. Future work will expand our machine learning framework to 3D volumes, improve our feature extraction in hr-CBCT and clinically validate this model. Early detection of microfractures will lead to more appropriate treatment and longer tooth retention.