Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT

Objectives: The objective of this study is the development and validation of a novel artificial intelligence driven tool for fast and accurate mandibular canal segmentation on cone beam computed tomography (CBCT). Methods: A total of 235 CBCT scans from dentate subjects needing oral surgery were used in this study, allowing for development, training and validation of a deep learning algorithm for automated mandibular canal (MC) segmentation on CBCT. Shape, diameter and direction of the MC were adjusted on all CBCT slices using a voxelwise approach. Validation was then performed on a random set of 30 CBCTs - previously unseen by the algorithm - where voxel-level annotations allowed for assessment of all MC segmentations. Results: Primary results show successful implementation of the AI algorithm for segmentation of the MC with a mean IoU of 0.636 (+/- 0.081), a median IoU of 0.639 (+/- 0.081), a mean Dice Similarity Coefficient of 0.774 (+/- 0.062). Precision, recall and accuracy had mean values of 0.782 (+/- 0.121), 0.792 (+/- 0.108) and 0.99 (+/- 7.64 x10(-05)) respectively. The total time for automated AI segmentation was 21.26 s (+/- 2.79), which is 107 times faster than accurate manual segmentation. Conclusions: This study demonstrates a novel, fast and accurate AI-driven module for MC segmentation on CBCT. Clinical Significance: Given the importance of adequate pre-operative mandibular canal assessment, Artificial Intelligence could help relieve practitioners from the delicate and time-consuming task of manually tracing and segmenting this structure, helping prevent per- and post-operative neurovascular complications.

Automated summary

This study developed and validated a novel AI-driven tool for fast and accurate mandibular canal segmentation on CBCT scans. Using a deep learning algorithm trained on 235 CBCT scans, the automated segmentation demonstrated high precision and speed, providing a potential solution to relieve practitioners from manual segmentation tasks.