  Preoperative radiological identification of mandibular canals is essential
for maxillofacial surgery. This study demonstrates the reproducibility of a
deep learning system (DLS) by evaluating its localisation performance on 165
heterogeneous cone beam computed tomography (CBCT) scans from 72 patients in
comparison to an experienced radiologist's annotations. We evaluated the
performance of the DLS using the symmetric mean curve distance (SMCD), the
average symmetric surface distance (ASSD), and the Dice similarity coefficient
(DSC). The reproducibility of the SMCD was assessed using the within-subject
coefficient of repeatability (RC). Three other experts rated the diagnostic
validity twice using a 0-4 Likert scale. The reproducibility of the Likert
scoring was assessed using the repeatability measure (RM). The RC of SMCD was
0.969 mm, the median (interquartile range) SMCD and ASSD were 0.643 (0.186) mm
and 0.351 (0.135) mm, respectively, and the mean (standard deviation) DSC was
0.548 (0.138). The DLS performance was most affected by postoperative changes.
The RM of the Likert scoring was 0.923 for the radiologist and 0.877 for the
DLS. The mean (standard deviation) Likert score was 3.94 (0.27) for the
radiologist and 3.84 (0.65) for the DLS. The DLS demonstrated proficient
qualitative and quantitative reproducibility, temporal generalisability, and
clinical validity.
