  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, 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.
