Dental student application of artificial intelligence technology in detecting proximal caries lesions

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dc.authoridBayraktar, Yusuf/0000-0001-6250-5651
dc.authoridAYHAN, BATURALP/0000-0002-7488-895X
dc.contributor.authorAyan, Enes
dc.contributor.authorBayraktar, Yusuf
dc.contributor.authorCelik, Cigdem
dc.contributor.authorAyhan, Baturalp
dc.date.accessioned2025-01-21T16:37:19Z
dc.date.available2025-01-21T16:37:19Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractObjectives: This study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network.Methods: A total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called You Only Look Once, was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre-test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post-test results of both groups were recorded. The labeling duration of the students was also measured and analyzed.Results: When both groups' pre-test and post-test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p < 0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non-trained group in terms of post-test scores (p < 0.05). In group 2 (trained group), the post-test labeling time was considerably increased (p < 0.05).Conclusions: The students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.
dc.identifier.doi10.1002/jdd.13437
dc.identifier.endpage500
dc.identifier.issn0022-0337
dc.identifier.issn1930-7837
dc.identifier.issue4
dc.identifier.pmid38200405
dc.identifier.scopus2-s2.0-85181878186
dc.identifier.scopusqualityQ2
dc.identifier.startpage490
dc.identifier.urihttps://doi.org/10.1002/jdd.13437
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24453
dc.identifier.volume88
dc.identifier.wosWOS:001140022200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Dental Education
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectartificial intelligence; bitewing radiography; convolutional neural networks; deep learning; dental caries
dc.titleDental student application of artificial intelligence technology in detecting proximal caries lesions
dc.typeArticle

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