Publication:
Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs

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dc.contributor.authorAKGÜN, GAZİ
dc.contributor.authorsGülüm S., Kutal S., AYDIN K., AKGÜN G., Akdağ A.
dc.date.accessioned2023-07-17T09:00:23Z
dc.date.available2023-07-17T09:00:23Z
dc.date.issued2023-01-01
dc.description.abstractObjective: This study aims to investigate the effect of number of data on model performance, for the detection of tooth numbering problem on dental panoramic radiographs, with the help of image processing and deep learning algorithms. Study Design: The data set consists of 3000 anonymous dental panoramic X-rays of adult individuals. Panoramic X-rays were labeled on the basis of 32 classes in line with the FDI tooth numbering system. In order to examine the relationship between the number of data used in image processing algorithms and model performance, four different datasets which include 1000, 1500, 2000 and 2500 panoramic X-rays, were used. The training of the models was carried out with the YOLOv4 algorithm and trained models were tested on a fixed test dataset with 500 data and compared based on F1 score, mAP, sensitivity, precision and recall metrics. Results: The performance of the model increased as the number of data used during the training of the model increased. Therefore, the last model trained with 2500 data showed the highest success among all the trained models. Conclusion: Dataset size is important for dental enumeration, and large samples should be considered as more reliable.
dc.identifier.citationGülüm S., Kutal S., AYDIN K., AKGÜN G., Akdağ A., "Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs", Oral Radiology, 2023
dc.identifier.doi10.1007/s11282-023-00689-4
dc.identifier.issn0911-6028
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85163987274&origin=inward
dc.identifier.urihttps://hdl.handle.net/11424/291317
dc.language.isoeng
dc.relation.ispartofOral Radiology
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectTıp
dc.subjectDahili Tıp Bilimleri
dc.subjectNükleer Tıp
dc.subjectDiş Hekimliği
dc.subjectSağlık Bilimleri
dc.subjectMedicine
dc.subjectInternal Medicine Sciences
dc.subjectNuclear medicine
dc.subjectDentistry
dc.subjectHealth Sciences
dc.subjectKlinik Tıp (MED)
dc.subjectKlinik Tıp
dc.subjectDİŞ HEKİMLİĞİ, ORAL CERRAHİ VE TIP
dc.subjectRADYOLOJİ, NÜKLEER TIP ve MEDİKAL GÖRÜNTÜLEME
dc.subjectClinical Medicine (MED)
dc.subjectCLINICAL MEDICINE
dc.subjectDENTISTRY, ORAL SURGERY & MEDICINE
dc.subjectRADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
dc.subjectDiş Hekimliği (çeşitli)
dc.subjectRadyoloji, Nükleer Tıp ve Görüntüleme
dc.subjectDentistry (miscellaneous)
dc.subjectRadiology, Nuclear Medicine and Imaging
dc.subjectArtificial intelligence
dc.subjectHealth technologies
dc.subjectImage processing
dc.subjectPanoramic x-ray
dc.subjectTooth numbering
dc.titleEffect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs
dc.typearticle
dspace.entity.typePublication
local.avesis.ida8726665-0257-4509-9709-3984e2761eb3
local.indexed.atPUBMED
local.indexed.atSCOPUS
relation.isAuthorOfPublication4cca1569-333a-440f-9f9e-7cf897217af2
relation.isAuthorOfPublication.latestForDiscovery4cca1569-333a-440f-9f9e-7cf897217af2

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