Automatic Identification of Dental Implant Brands with Deep Learning Algorithms

Basit Öğe Kaydı
dc.contributor.authorYüce, H.
dc.contributor.authorAçıkgöz, G.
dc.contributor.authorAl-Jumaili, S.
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorUçan, G. Özlü
dc.contributor.authorDere, K. A.
dc.date.accessioned2025-08-14T14:02:58Z
dc.date.available2025-08-14T14:02:58Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
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 four different dental implant systems (NucleOSS, Medentika, Nobel, and Implance) were used from a total of 5,375 cropped panoramic radiographs. To enhance image clarity and reduce blurriness, the Contrast Limited Adaptive Histogram Equalization (CLAHE) filter was applied. GoogleNet, ResNet-18, VGG16, and ShuffleNet deep learning algorithms were utilized to classify the four different dental implant systems. To evaluate the classification performance of the algorithms, 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 < 0.001, p < 0.001, and p < 0.001, 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.citationYuce, H., Acıkgoz, G., Al-Jumaili, S., Ucan, O. N., Ozlu Ucan, G., & Dere, K. A. (2025). Automatic Identification of Dental Implant Brands with Deep Learning Algorithms. Dentomaxillofacial Radiology, twaf054.
dc.identifier.doi10.1093/dmfr/twaf054
dc.identifier.issn0250-832X
dc.identifier.issn1476-542X
dc.identifier.pmid40627380
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5848
dc.indekslendigikaynakPubMed
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.publisherUniversity Press Erlangen
dc.relation.ispartofDento Maxillo Facial Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectautomatic identification
dc.subjectdeep learning
dc.subjectdental implant
dc.titleAutomatic Identification of Dental Implant Brands with Deep Learning Algorithms
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

PubMed İndeksli Yayınlar Koleksiyonu
Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu