AI-powered segmentation of bifid mandibular canals using CBCT.
Authors
Affiliations (6)
Affiliations (6)
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya University, Mithatpaşa Mah. Adnan Menderes Cad. No:122/B, Adapazarı, Sakarya, 54100, Turkey.
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey.
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya University, Mithatpaşa Mah. Adnan Menderes Cad. No:122/B, Adapazarı, Sakarya, 54100, Turkey. [email protected].
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey.
- Department of Mathematics-Computer, Faculty of Science, Eskişehir Osmangazi University, Eskişehir, Turkey.
- Department of Oral and Maxillofacial Medicine and Diagnostic Sciences, School of Dental Medicine, Case Western Reserve University, Cleveland, OH, USA.
Abstract
Accurate segmentation of the mandibular and bifid canals is crucial in dental implant planning to ensure safe implant placement, third molar extractions and other surgical interventions. The objective of this study is to develop and validate an innovative artificial intelligence tool for the efficient, and accurate segmentation of the mandibular and bifid canals on CBCT. CBCT data were screened to identify patients with clearly visible bifid canal variations, and their DICOM files were extracted. These DICOM files were then imported into the 3D Slicer<sup>®</sup> open-source software, where bifid canals and mandibular canals were annotated. The annotated data, along with the raw DICOM files, were processed using the nnU-Netv2 training model by CranioCatch AI software team. 69 anonymized CBCT volumes in DICOM format were converted to NIfTI file format. The method, utilizing nnU-Net v2, accurately predicted the voxels associated with the mandibular canal, achieving an intersection of over 50% in nearly all samples. The accuracy, Dice score, precision, and recall scores for the mandibular canal/bifid canal were determined to be 0.99/0.99, 0.82/0.46, 0.85/0.70, and 0.80/0.42, respectively. Despite the bifid canal segmentation not meeting the expected level of success, the findings indicate that the proposed method shows promising and has the potential to be utilized as a supplementary tool for mandibular canal segmentation. Due to the significance of accurately evaluating the mandibular canal before surgery, the use of artificial intelligence could assist in reducing the burden on practitioners by automating the complicated and time-consuming process of tracing and segmenting this structure. Being able to distinguish bifid channels with artificial intelligence will help prevent neurovascular problems that may occur before or after surgery.