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

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dc.authorid0000-0002-6429-4197
dc.contributor.authorGülüm, Semih
dc.contributor.authorKutal, Seçilay
dc.contributor.authorCesur Aydın, Kader
dc.contributor.authorAkgün, Gazi
dc.contributor.authorAkdağ, Aleyna
dc.date.accessioned2024-01-17T11:56:36Z
dc.date.available2024-01-17T11:56:36Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Diş Hekimliği Fakültesi, Ağız, Diş ve Çene Radyolojisi Ana Bilim Dalı
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., Cesur Aydın, K., Akgün, G. ve Akdağ, A. (2023). Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs. Oral Radiology, 39(4), 715-721. https://dx.doi.org/10.1007/s11282-023-00689-4
dc.identifier.doi10.1007/s11282-023-00689-4
dc.identifier.endpage721
dc.identifier.issn0911-6028
dc.identifier.issn1613-9674
dc.identifier.issue4
dc.identifier.pmid37405624
dc.identifier.scopus2-s2.0-85163987274
dc.identifier.scopusqualityQ2
dc.identifier.startpage715
dc.identifier.urihttps://dx.doi.org/10.1007/s11282-023-00689-4
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12164
dc.identifier.volume39
dc.identifier.wos001019726600001en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorCesur Aydın, Kader
dc.institutionauthorAkdağ, Aleyna
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofOral Radiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectArtifcial Intelligence
dc.subjectImage Processing
dc.subjectHealth Technologies
dc.subjectPanoramic X-Ray
dc.subjectTooth Numbering
dc.titleEffect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs
dc.typeArticle

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