Automatic identification of dental implant brands with deep learning algorithms

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dc.contributor.authorYüce, Hasret
dc.contributor.authorAçıkgöz, Gözde
dc.contributor.authorAl-Jumaili, Saif
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorUçan, Gülfem Özlü
dc.contributor.authorDere, Kadriye Ayça
dc.date.accessioned2025-08-25T06:59:35Z
dc.date.available2025-08-25T06:59:35Z
dc.date.issued2025
dc.departmentDiş Hekimliği Fakültesi
dc.description.abstractObjectives: To reduce the problems arising from the inability to identify dental implant brands, this study aims to classify various dental implant brands using deep learning algorithms on panoramic radiographs. Methods: Images of 4 different dental implant systems (NucleOSS, Medentika, Nobel, and Implance) were used from a total of 5375 cropped panoramic radiographs. To enhance image clarity and reduce blurriness, the contrast limited adaptive histogram equalization filter was applied. GoogleNet, ResNet-18, VGG16, and ShuffleNet deep learning algorithms were utilized to classify the 4 different dental implant systems. To evaluate the classification performance of the algorithms, Receiver Operating Characteristic (ROC) curves and confusion matrices were generated. Based on these confusion matrices, accuracy, precision, sensitivity, and F1 score were calculated. The Z-test was used to compare the performance metrics across different algorithms. Results: The accuracy rates of the deep learning algorithms were obtained as 96.00% for GoogleNet, 84.40% for ResNet-18, 98.90% for VGG16, and 84.80% for ShuffleNet. A statistically significant difference was found between the accuracy rate of the VGG16 algorithm and those of GoogleNet, ShuffleNet, and ResNet-18 (P < .001, P < .001, and P < .001, respectively). Conclusions: With the achievement of high accuracy rates, deep learning algorithms are considered a valuable and powerful method for identifying dental implant brands.
dc.identifier.doi10.1093/dmfr/twaf054
dc.identifier.issn0250-832X
dc.identifier.issn1476-542X
dc.identifier.urihttps://hdl.handle.net/11363/10303
dc.identifier.wos001541638600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.institutionauthorUçan, Gülfem Özlü
dc.language.isoen
dc.publisherOXFORD UNIV PRESS, GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
dc.relation.ispartofDENTOMAXILLOFACIAL RADIOLOGY
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectautomatic identification
dc.subjectdeep learning
dc.subjectdental implant
dc.titleAutomatic identification of dental implant brands with deep learning algorithms
dc.typeArticle

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