A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs

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dc.authoridUZEN, Huseyin/0000-0002-0998-2130
dc.authoridduman, suayip burak/0000-0003-2552-0187
dc.authoridDAYI, Burak/0000-0002-5289-438X
dc.authorwosidUZEN, Huseyin/CZK-0841-2022
dc.authorwosidduman, suayip burak/ABE-5878-2020
dc.authorwosidDAYI, Burak/ABH-6633-2020
dc.contributor.authorDayi, Burak
dc.contributor.authorUzen, Huseyin
dc.contributor.authorCicek, Ipek Balikci
dc.contributor.authorDuman, Suayip Burak
dc.date.accessioned2024-08-04T20:53:22Z
dc.date.available2024-08-04T20:53:22Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry's Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.en_US
dc.identifier.doi10.3390/diagnostics13020202
dc.identifier.issn2075-4418
dc.identifier.issue2en_US
dc.identifier.pmid36673010en_US
dc.identifier.scopus2-s2.0-85146753065en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13020202
dc.identifier.urihttps://hdl.handle.net/11616/101133
dc.identifier.volume13en_US
dc.identifier.wosWOS:000914423500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcaries diagnosisen_US
dc.subjectconvolutional neural networken_US
dc.subjectdental panoramic radiographsen_US
dc.subjectdeep learningen_US
dc.titleA Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographsen_US
dc.typeArticleen_US

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