Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images

Basit Öğe Kaydı
dc.authorid0000-0002-1014-4417
dc.authorid0000-0001-5932-8068
dc.authorid0000-0001-5036-9867
dc.authorid0000-0001-9677-8690
dc.authorid0000-0002-6655-8002
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.contributor.authorElfayome, Nermin Sameh
dc.contributor.authorHussien, Reham Ashraf
dc.contributor.authorGulsen, Ibrahim Tevfik
dc.contributor.authorKuran, Alican
dc.contributor.authorGunes, Ihsan
dc.contributor.authorAl-Badr, Alwaleed
dc.date.accessioned2026-01-24T12:28:59Z
dc.date.available2026-01-24T12:28:59Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractObjectives The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.Methods In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values.Results F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.Conclusions Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.
dc.identifier.doi10.1093/dmfr/twae012
dc.identifier.endpage266
dc.identifier.issn0250-832X
dc.identifier.issn1476-542X
dc.identifier.issue4
dc.identifier.pmid38502963
dc.identifier.scopus2-s2.0-85191897276
dc.identifier.scopusqualityQ1
dc.identifier.startpage256
dc.identifier.urihttps://doi.org/10.1093/dmfr/twae012
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5037
dc.identifier.volume53
dc.identifier.wosWOS:001196649900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofDentomaxillofacial Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectartificial intelligence
dc.subjectcomputer modelling
dc.subjectdeep learning
dc.subjectCBCT
dc.subjectmaxillary sinus
dc.titleArtificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images
dc.typeArticle

Dosyalar

Koleksiyon

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