AI-powered segmentation of bifid mandibular canals using CBCT
Abstract Objective 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 t...
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BMC
2025-06-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-06311-9 |
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| author | Ismail Gumussoy Kardelen Demirezer Suayip Burak Duman Emre Haylaz Ibrahim Sevki Bayrakdar Ozer Celik Ali Zakir Syed |
| author_facet | Ismail Gumussoy Kardelen Demirezer Suayip Burak Duman Emre Haylaz Ibrahim Sevki Bayrakdar Ozer Celik Ali Zakir Syed |
| author_sort | Ismail Gumussoy |
| collection | DOAJ |
| description | Abstract Objective 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. Materials and methods 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® 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. Results 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. Conclusions 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. Clinical relevance Being able to distinguish bifid channels with artificial intelligence will help prevent neurovascular problems that may occur before or after surgery. |
| format | Article |
| id | doaj-art-10d91a42541a4dcdb5dd8ae475c7353e |
| institution | DOAJ |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-10d91a42541a4dcdb5dd8ae475c7353e2025-08-20T03:10:32ZengBMCBMC Oral Health1472-68312025-06-012511910.1186/s12903-025-06311-9AI-powered segmentation of bifid mandibular canals using CBCTIsmail Gumussoy0Kardelen Demirezer1Suayip Burak Duman2Emre Haylaz3Ibrahim Sevki Bayrakdar4Ozer Celik5Ali Zakir Syed6Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya UniversityDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu UniversityDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu UniversityDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya UniversityDepartment of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi UniversityDepartment of Mathematics-Computer, Faculty of Science, Eskişehir Osmangazi UniversityDepartment of Oral and Maxillofacial Medicine and Diagnostic Sciences, School of Dental Medicine, Case Western Reserve UniversityAbstract Objective 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. Materials and methods 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® 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. Results 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. Conclusions 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. Clinical relevance Being able to distinguish bifid channels with artificial intelligence will help prevent neurovascular problems that may occur before or after surgery.https://doi.org/10.1186/s12903-025-06311-9Artificial intelligenceCone beam computed tomographyBifid mandibular canalMandibular canal |
| spellingShingle | Ismail Gumussoy Kardelen Demirezer Suayip Burak Duman Emre Haylaz Ibrahim Sevki Bayrakdar Ozer Celik Ali Zakir Syed AI-powered segmentation of bifid mandibular canals using CBCT BMC Oral Health Artificial intelligence Cone beam computed tomography Bifid mandibular canal Mandibular canal |
| title | AI-powered segmentation of bifid mandibular canals using CBCT |
| title_full | AI-powered segmentation of bifid mandibular canals using CBCT |
| title_fullStr | AI-powered segmentation of bifid mandibular canals using CBCT |
| title_full_unstemmed | AI-powered segmentation of bifid mandibular canals using CBCT |
| title_short | AI-powered segmentation of bifid mandibular canals using CBCT |
| title_sort | ai powered segmentation of bifid mandibular canals using cbct |
| topic | Artificial intelligence Cone beam computed tomography Bifid mandibular canal Mandibular canal |
| url | https://doi.org/10.1186/s12903-025-06311-9 |
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