Evaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural network

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dc.authoridYesil, Cagri/0000-0002-8961-2673
dc.authorwosidSerindere, Gozde/HPH-0265-2023
dc.contributor.authorSerindere, Gozde
dc.contributor.authorBilgili, Ersen
dc.contributor.authorYesil, Cagri
dc.contributor.authorOzveren, Neslihan
dc.date.accessioned2024-06-12T10:52:34Z
dc.date.available2024-06-12T10:52:34Z
dc.date.issued2022
dc.departmentTrakya Üniversitesien_US
dc.description.abstractPurpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the gold standard for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.en_US
dc.identifier.doi10.5624/isd.20210263
dc.identifier.endpage195en_US
dc.identifier.issn2233-7822
dc.identifier.issn2233-7830
dc.identifier.issue2en_US
dc.identifier.pmid35799961en_US
dc.identifier.scopus2-s2.0-85130311976en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage187en_US
dc.identifier.urihttps://doi.org/10.5624/isd.20210263
dc.identifier.urihttps://hdl.handle.net/20.500.14551/18760
dc.identifier.volume52en_US
dc.identifier.wosWOS:000772954800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherKorean Acad Oral & Maxillofacial Radiologyen_US
dc.relation.ispartofImaging Science In Dentistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectMaxillary Sinusitisen_US
dc.subjectPanoramic Radiographyen_US
dc.subjectCone-Beam Computed Tomographyen_US
dc.subjectIncidental Findingsen_US
dc.subjectRhinosinusitisen_US
dc.subjectPerformanceen_US
dc.subjectCten_US
dc.titleEvaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural networken_US
dc.typeArticleen_US

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