Detection of pulpal calcifications on bite-wing radiographs using deep learning

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dc.contributor.authorYuce, Fatma
dc.contributor.authorOzic, Muhammet Usame
dc.contributor.authorTassoker, Melek
dc.date.accessioned2024-02-23T13:55:53Z
dc.date.available2024-02-23T13:55:53Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractObjectives Pulpal calcifications are discrete hard calcified masses of varying sizes in the dental pulp cavity. This study is aimed at measuring the performance of the YOLOv4 deep learning algorithm to automatically determine whether there is calcification in the pulp chambers in bite-wing radiographs. Materials and methods In this study, 2000 bite-wing radiographs were collected from the faculty database. The oral radiologists labeled the pulp chambers on the radiographs as Present and Absent according to whether there was calcification. The data were randomly divided into 80% training, 10% validation, and 10% testing. The weight file for pulpal calcification was obtained by training the YOLOv4 algorithm with the transfer learning method. Using the weights obtained, pulp chambers and calcifications were automatically detected on the test radiographs that the algorithm had never seen. Two oral radiologists evaluated the test results, and performance criteria were calculated. Results The results obtained on the test data were evaluated in two stages: detection of pulp chambers and detection of pulpal calcification. The detection performance of pulp chambers was as follows: recall 86.98%, precision 98.94%, F1-score 91.60%, and accuracy 86.18%. Pulpal calcification Absent and Present detection performance was as follows: recall 86.39%, precision 85.23%, specificity 97.94%, F1-score 85.49%, and accuracy 96.54%. Conclusion The YOLOv4 algorithm trained with bite-wing radiographs detected pulp chambers and calcification with high success rates.en_US
dc.identifier.doi10.1007/s00784-022-04839-6
dc.identifier.endpage2689en_US
dc.identifier.issn1432-6981
dc.identifier.issn1436-3771
dc.identifier.issue6en_US
dc.identifier.pmid36564651en_US
dc.identifier.scopus2-s2.0-85144652217en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2679en_US
dc.identifier.urihttps://doi.org/10.1007/s00784-022-04839-6
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11002
dc.identifier.volume27en_US
dc.identifier.wosWOS:000903465700001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofClinical Oral Investigationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectBite-Wingen_US
dc.subjectDeep Learningen_US
dc.subjectPulpal Calcificationen_US
dc.subjectYolov4en_US
dc.titleDetection of pulpal calcifications on bite-wing radiographs using deep learningen_US
dc.typeArticleen_US

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