Segmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images

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dc.authorid0000-0001-8477-6807
dc.authorid0000-0001-5036-9867
dc.authorid0000-0002-0281-6981
dc.authorid0000-0001-6768-0176
dc.contributor.authorDeniz, Hatice Ahsen
dc.contributor.authorBayrakdar, İbrahim Şevki
dc.contributor.authorNalçacı, Rana
dc.contributor.authorOrhan, Kaan
dc.date.accessioned2025-07-09T07:28:17Z
dc.date.available2025-07-09T07:28:17Z
dc.date.issued2025
dc.departmentDiş Hekimliği Fakültesi
dc.description.abstractObjectives: The nasopalatine canal (NPC) is an anatomical formation with varying morphology. NPC can be visualized using the cone-beam computed tomography (CBCT). Also, CBCT has been used in many studies on artificial intelligence (AI). The “You only look once” (YOLO) is an AI framework that stands out with its speed. This study compared the observer and AI regarding the NPC segmentation and assessment of the NPC furcation status in CBCT images. Methods: In this study, axial sections of 200 CBCT images were used. These images were labeled and evaluated for the absence or presence of the NPC furcation. These images were then divided into three; 160 images were used as the training dataset, 20 as the validation dataset, and 20 as the test dataset. The training was performed by making 800 epochs using the YOLOv5x-seg model. Results: Sensitivity, Precision, F1 score, IoU, mAP, and AUC values were determined for NPC detection, segmentation, and classification of the YOLOv5x-seg model. The values were found to be 0.9680, 0.9953, 0.9815, 0.9636, 0.7930, and 0.8841, respectively, for the group with the absence of the NPC furcation; and 0.9827, 0.9975, 0.9900, 0.9803, 0.9637, and 0.9510, for the group with the presence of the NPC furcation. Conclusions: Our results showed that even when the YOLOv5x-seg model is trained with the NPC furcation and fewer datasets, it achieves sufficient prediction accuracy. The segmentation feature of the YOLOv5 algorithm, which is based on an object detection algorithm, has achieved quite successful results despite its recent development.
dc.identifier.doi10.1007/s11282-025-00812-7
dc.identifier.endpage413
dc.identifier.issn09116028
dc.identifier.issue3
dc.identifier.pmid40021578
dc.identifier.startpage403
dc.identifier.urihttps://dx.doi.org/10.1007/s11282-025-00812-7
dc.identifier.urihttps://hdl.handle.net/20.500.12451/13214
dc.identifier.volume41
dc.identifier.wosWOS:001434067300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorDeniz, Haticem Ahsen
dc.institutionauthorid0000-0001-8477-6807
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofOral Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Intelligence
dc.subjectCone-beam Computed Tomography
dc.subjectDeep Learning
dc.subjectNasopalatine Canal
dc.subjectSegmentation
dc.titleSegmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images
dc.typeArticle

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