Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT
P Lahoud, S Diels, L Niclaes, S Van Aelst, H Willems… - Journal of dentistry, 2022 - Elsevier
Journal of dentistry, 2022•Elsevier
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 …
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 …
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 voxel-wise 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 (± 7.64×10−05) 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.
Elsevier
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