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

Pierre Lahoud; Siebe Diels; Liselot Niclaes; Stijn Van Aelst; Holger Willems; Adriaan Van Gerven; Marc Quirynen; Reinhilde Jacobs

doi:10.1016/j.jdent.2021.103891

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

J Dent. 2022 Jan:116:103891. doi: 10.1016/j.jdent.2021.103891. Epub 2021 Nov 13.

Authors

Pierre Lahoud¹, Siebe Diels², Liselot Niclaes³, Stijn Van Aelst³, Holger Willems², Adriaan Van Gerven², Marc Quirynen⁴, Reinhilde Jacobs⁵

Affiliations

¹ OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium; Department of Oral Health Sciences, Periodontology and Oral Microbiology, University Hospitals of Leuven, Belgium. Electronic address: pierre.lahoud@kuleuven.be.
² Relu BV, Leuven, Belgium.
³ OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium.
⁴ Department of Oral Health Sciences, Periodontology and Oral Microbiology, University Hospitals of Leuven, Belgium.
⁵ OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium; Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.

PMID: 34780873
DOI: 10.1016/j.jdent.2021.103891

Abstract

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.

Keywords: Artificial intelligence; Cone-beam computerized tomography; Inferior alveolar nerve; Neural network models.

MeSH terms

Artificial Intelligence
Cone-Beam Computed Tomography
Deep Learning*
Humans
Image Processing, Computer-Assisted
Mandibular Canal
Spiral Cone-Beam Computed Tomography*