Dental microfracture detection using wavelet features and machine learning

Jared Vicory; Ramraj Chandradevan; Pablo Hernandez-Cerdan; Wei Angel Huang; Dani Fox; Laith Abu Qdais; Matthew McCormick; Andre Mol; Rick Walter; J. S. Marron; Hassem Geha; Asma Khan; Beatriz Paniagua

doi:10.1117/12.2580744

15 February 2021 Dental microfracture detection using wavelet features and machine learning

Jared Vicory, Ramraj Chandradevan, Pablo Hernandez-Cerdan, Wei Angel Huang, Dani Fox, Laith Abu Qdais, Matthew McCormick, Andre Mol, Rick Walter, J. S. Marron, Hassem Geha, Asma Khan, Beatriz Paniagua

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115961R (2021) https://doi.org/10.1117/12.2580744
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

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Jared Vicory, Ramraj Chandradevan, Pablo Hernandez-Cerdan, Wei Angel Huang, Dani Fox, Laith Abu Qdais, Matthew McCormick, Andre Mol, Rick Walter, J. S. Marron, Hassem Geha, Asma Khan, and Beatriz Paniagua "Dental microfracture detection using wavelet features and machine learning", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115961R (15 February 2021); https://doi.org/10.1117/12.2580744

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