AI-powered segmentation of bifid mandibular canals using CBCT

Basit Öğe Kaydı
dc.contributor.authorGumussoy, Ismail
dc.contributor.authorDemirezer, Kardelen
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorHaylaz, Emre
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorCelik, Ozer
dc.contributor.authorSyed, Ali Zakir
dc.date.accessioned2026-04-04T13:33:08Z
dc.date.available2026-04-04T13:33:08Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractObjectiveAccurate 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.Materials and methodsCBCT 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 (R) 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.Results69 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.ConclusionsDespite 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.Clinical relevanceBeing able to distinguish bifid channels with artificial intelligence will help prevent neurovascular problems that may occur before or after surgery.
dc.identifier.doi10.1186/s12903-025-06311-9
dc.identifier.issn1472-6831
dc.identifier.issue1
dc.identifier.pmid40468302
dc.identifier.scopus2-s2.0-105007308200
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1186/s12903-025-06311-9
dc.identifier.urihttps://hdl.handle.net/11616/108954
dc.identifier.volume25
dc.identifier.wosWOS:001503057400004
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBmc
dc.relation.ispartofBmc Oral Health
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectArtificial intelligence
dc.subjectCone beam computed tomography
dc.subjectBifid mandibular canal
dc.subjectMandibular canal
dc.titleAI-powered segmentation of bifid mandibular canals using CBCT
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu