Artificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs

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dc.authoridBALEL, YUNUS/0000-0003-0496-8564
dc.authoridSagtas, Kaan/0000-0003-4689-7020
dc.contributor.authorBalel, Yunus
dc.contributor.authorSagtas, Kaan
dc.contributor.authorTeke, Fatih
dc.contributor.authorKurt, Mehmet Ali
dc.date.accessioned2025-05-04T16:46:52Z
dc.date.available2025-05-04T16:46:52Z
dc.date.issued2025
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractObjectivesThis study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.Materials and MethodsA retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized. Two deep-learning models were trained using the YOLOv8 algorithm. The first model classified the regions of the jaw to number the teeth and identify implant regions, while the second model performed implant segmentation. Performance metrics including precision, recall, and F1-score were used to evaluate the model's effectiveness.ResultsThe implant segmentation model achieved a precision of 91.4%, recall of 90.5%, and an F1-score of 93.1%. For the implant-numbering task, precision ranged from 0.94 to 0.981, recall from 0.895 to 0.956, and F1-scores from 0.917 to 0.966 across various jaw regions. The analysis revealed that implants were most frequently located in the maxillary posterior region.ConclusionsThe AI model demonstrated high accuracy in detecting and numbering dental implants in panoramic radiographs. This technology offers the potential to reduce clinicians' workload and improve diagnostic accuracy in dental implantology. Further validation across more diverse datasets is recommended to enhance its clinical applicability.Clinical RelevanceThis AI model could revolutionize dental implant detection and classification, providing fast, objective analyses to support clinical decision-making in dental practices.
dc.identifier.doi10.1111/cid.70000
dc.identifier.issn1523-0899
dc.identifier.issn1708-8208
dc.identifier.issue1
dc.identifier.pmid39846131
dc.identifier.scopus2-s2.0-85216011254
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1111/cid.70000
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35373
dc.identifier.volume27
dc.identifier.wosWOS:001402700300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofClinical Implant Dentistry and Related Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectartificial intelligence
dc.subjectdeep learning
dc.subjectdental implant detection
dc.subjectimplant numbering
dc.subjectorthopantomograph analysis
dc.titleArtificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs
dc.typeArticle

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