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<span class="release-stage" >pre-print</span>
Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging. However, interpretation of CBCT images is a time-consuming process that requires a physician to work with complicated software. In this work we propose an automated pipeline composed of several deep convolutional neural networks and algorithmic heuristics. Our task is two-fold: a) find locations of each present tooth inside a 3D image volume,<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1810.10309v1">arXiv:1810.10309v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ys6n7ziggrdlhds2necfqislje">fatcat:ys6n7ziggrdlhds2necfqislje</a> </span>
more »... and b) detect several common tooth conditions in each tooth. The proposed system achieves 96.3\% accuracy in tooth localization and an average of 0.94 AUROC for 6 common tooth conditions.
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