Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs

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dc.authorid0000-0001-6768-0176en_US
dc.authorid0000-0001-7780-3395en_US
dc.authorid0000-0003-0819-4578en_US
dc.contributor.authorOrhan, Kaan
dc.contributor.authorBelgin, Ceren Aktuna
dc.contributor.authorManulis, David
dc.contributor.authorGolitsyna, Maria
dc.contributor.authorBayrak, Seval
dc.contributor.authorAksoy, Seçil
dc.date.accessioned2024-06-06T08:54:33Z
dc.date.available2024-06-06T08:54:33Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Diş Hekimliği Fakültesi, Ağız, Diş ve Çene Radyolojisi Ana Bilim Dalıen_US
dc.description.abstractPurpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations. Materials and Methods: PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.en_US
dc.identifier.citationOrhan, K., Belgin, C. A., Manulis, D., Golitsyna, M., Bayrak, S., Aksoy, S., ... & Shlenskii, V. (2023). Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs. Imaging Science in Dentistry, 53(3), 199.en_US
dc.identifier.doi10.5624/isd.20230109
dc.identifier.endpage207en_US
dc.identifier.issn2233-7822
dc.identifier.issn2233-7830
dc.identifier.issue3en_US
dc.identifier.pmid37799743en_US
dc.identifier.scopus2-s2.0-85173042648en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage199en_US
dc.identifier.urihttp://dx.doi.org/10.5624/isd.20230109
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12203
dc.identifier.volume53en_US
dc.identifier.wosWOS:001047863200001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorBayrak, Seval
dc.language.isoenen_US
dc.publisherKorean Acad Oral & Maxillofacial Radiologyen_US
dc.relation.ispartofImaging Science In Dentistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectRadiographyen_US
dc.subjectPanoramicen_US
dc.subjectDeep Learningen_US
dc.subjectDentistryen_US
dc.subjectConvolutional Neural-Networksen_US
dc.titleDetermining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographsen_US
dc.typeArticleen_US

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