Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning

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dc.contributor.authorAltun, Oguzhan
dc.contributor.authorOzen, Duygu Celik
dc.contributor.authorDuman, Suayip Burak
dc.contributor.authorDedeoglu, Numan
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorEser, Gozde
dc.contributor.authorCelik, Ozer
dc.date.accessioned2026-04-04T13:33:08Z
dc.date.available2026-04-04T13:33:08Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractBackgroundMaxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries.ObjectiveThe aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images.MethodsData set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features.ResultsRecall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation.ConclusionThis study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.
dc.description.sponsorshipIdot;nn niversitesi
dc.description.sponsorshipThe authors declared no potential conflicts of interest for this article's research, authorship, and/or publication.
dc.identifier.doi10.1186/s12903-024-04924-0
dc.identifier.issn1472-6831
dc.identifier.issue1
dc.identifier.orcid0000-0003-2552-0187
dc.identifier.pmid39390490
dc.identifier.scopus2-s2.0-85206053211
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1186/s12903-024-04924-0
dc.identifier.urihttps://hdl.handle.net/11616/108960
dc.identifier.volume24
dc.identifier.wosWOS:001336903600001
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.subjectDeep learning
dc.subjectMaxillary sinus
dc.titleAutomatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning
dc.typeArticle

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