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

ABSTRACT: 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 repeatabi

SUBMITTER: Jarnstedt J 

PROVIDER: S-EPMC10465591 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature