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Preoperative radiological identification of mandibular canals is essentialfor maxillofacial surgery. This study demonstrates the reproducibility of adeep learning system (DLS) by evaluating its localisation performance on 165heterogeneous cone beam computed tomography (CBCT) scans from 72 patients incomparison to an experienced radiologist's annotations. We evaluated theperformance of the DLS using the symmetric mean curve distance (SMCD), theaverage symmetric surface distance (ASSD), and the Dice similarity coefficient(DSC). The reproducibility of the SMCD was assessed using the within-subjectcoefficient of repeatability (RC). Three other experts rated the diagnosticvalidity twice using a 0-4 Likert scale. The reproducibility of the Likertscoring was assessed using the repeatability measure (RM). The RC of SMCD was0.969 mm, the median (interquartile range) SMCD and ASSD were 0.643 (0.186) mmand 0.351 (0.135) mm, respectively, and the mean (standard deviation) DSC was0.548 (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 theDLS. The mean (standard deviation) Likert score was 3.94 (0.27) for theradiologist and 3.84 (0.65) for the DLS. The DLS demonstrated proficientqualitative and quantitative reproducibility, temporal generalisability, andclinical validity.

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset