  Deep learning approach has been demonstrated to automatically segment the
bilateral mandibular canals from CBCT scans, yet systematic studies of its
clinical and technical validation are scarce. To validate the mandibular canal
localization accuracy of a deep learning system (DLS) we trained it with 982
CBCT scans and evaluated using 150 scans of five scanners from clinical
workflow patients of European and Southeast Asian Institutes, annotated by four
radiologists. The interobserver variability was compared to the variability
between the DLS and the radiologists. In addition, the generalization of DLS to
CBCT scans from scanners not used in the training data was examined to evaluate
the out-of-distribution generalization capability. The DLS had lower
variability to the radiologists than the interobserver variability between them
and it was able to generalize to three new devices. For the radiologists'
consensus segmentation, used as gold standard, the DLS had a symmetric mean
curve distance of 0.39 mm compared to those of the individual radiologists with
0.62 mm, 0.55 mm, 0.47 mm, and 0.42 mm. The DLS showed comparable or slightly
better performance in the segmentation of the mandibular canal with the
radiologists and generalization capability to new scanners.

Machine Learning;Computer Vision and Pattern Recognition