Tooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study

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dc.authoridBilgir, Elif/0000-0001-9521-4682
dc.authoridBAYRAKDAR, Ibrahim Sevki/0000-0001-5036-9867
dc.authoridOrhan Sungur, Mukadder/0000-0002-0770-8904
dc.authoridCelik, Ozer/0000-0002-4409-3101
dc.authoridAYYILDIZ, HALIL/0000-0001-8633-1764
dc.contributor.authorAyyildiz, Halil
dc.contributor.authorOrhan, Mukadder
dc.contributor.authorBilgir, Elif
dc.contributor.authorCelik, Ozer
dc.contributor.authorBayrakdar, Ibrahim Sevki
dc.date.accessioned2025-03-09T10:49:05Z
dc.date.available2025-03-09T10:49:05Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractObjectivesAccurately identification and tooth numbering on radiographs is essential for any clinicians. The aim of the present study was to validate the hypothesis that Yolov5, a type of artificial intelligence model, can be trained to detect and number teeth in periapical radiographs.Materials and methodsSix thousand four hundred forty six anonymized periapical radiographs without motion-related artifacts were randomly selected from the database. All periapical radiographs in which all boundaries of any tooth could be distinguished were included in the study. The radiographic images used were randomly divided into three groups: 80% training, 10% validation, and 10% testing. The confusion matrix was used to examine model success.ResultsDuring the test phase, 2578 labelings were performed on 644 periapical radiographs. The number of true positive was 2434 (94.4%), false positive was 115 (4.4%), and false negative was 29 (1.2%). The recall, precision, and F1 scores were 0.9882, 0.9548, and 0.9712, respectively. Moreover, the model yielded an area under curve (AUC) of 0.603 on the receiver operating characteristic curve (ROC).ConclusionsThis study showed us that YOLOv5 is nearly perfect for numbering teeth on periapical radiography. Although high success rates were achieved as a result of the study, it should not be forgotten that artificial intelligence currently only can be guides dentists for accurate and rapid diagnosis.Clinical RelevanceIt is thought that dentists can accelerate the radiographic examination time and inexperienced dentists can reduce the error rate by using YOLOv5. Additionally, YOLOv5 can also be used in the education of dentistry students.
dc.identifier.doi10.1007/s00784-024-05999-3
dc.identifier.issn1432-6981
dc.identifier.issn1436-3771
dc.identifier.issue11
dc.identifier.pmid39448462
dc.identifier.scopus2-s2.0-85207350626
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s00784-024-05999-3
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4725
dc.identifier.volume28
dc.identifier.wosWOS:001340658300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofClinical Oral Investigations
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250310
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectPeriapical radiography
dc.subjectPolygonal segmentation
dc.subjectTooth numbering
dc.titleTooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study
dc.typeArticle

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