Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education

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dc.authorid0000-0002-4108-2968
dc.authorid0000-0003-2502-2698
dc.authorid0000-0003-0957-5348
dc.authorid0000-0003-0463-373X
dc.contributor.authorOguzhan, Aybeniz
dc.contributor.authorPeskersoy, Cem
dc.contributor.authorDevrimci, Elif Ercan
dc.contributor.authorKemaloglu, Hande
dc.contributor.authorOnder, Tolga Kagan
dc.date.accessioned2025-05-07T12:53:18Z
dc.date.available2025-05-07T12:53:18Z
dc.date.issued2024
dc.departmentEge Üniversitesi, Diş Hekimliği Fakültesi
dc.description.abstractPurpose and objective: Objective, valid, and reliable evaluations are needed in order to develop haptic skills in dental education. The aim of this study is to investigate the validity and reliability of the machine learning method in evaluating the haptic skills of dentistry students. Materials and methods: One-hundred fifty 6th semester dental students have performed Class II amalgam (C2A) and composite resin restorations (C2CR), in which all stages were evaluated with Direct Observation Practical Skills forms. The final phase was graded by three trainers and supervisors separately. Standard photographs of the restorations in the final stage were taken from different angles in a special setup and transferred to the Python program which utilized the Structural Similarity algorithm to calculate both the quantitative (numerical) and qualitative (visual) differences of each restoration. The validity and reliability analyses of inter-examiner evaluation were tested by Cronbach's Alpha and Kappa statistics (p = 0.05). Results: The intra-examiner reliability between Structural Similarity Index (SSIM) and examiners was found highly reliable in both C2A (alpha = 0.961) and C2CR (alpha = 0.856). The compatibility of final grades given by SSIM (53.07) and examiners (56.85) was statistically insignificant (p > 0.05). A significant difference was found between the examiners and SSIM when grading the occlusal surfaces in C2A and on the palatal surfaces of C2CR (p < 0.05). The concordance of observer assessments was found almost perfect in C2A (kappa = 0.806), and acceptable in C2CR (kappa = 0.769). Conclusion: Although deep machine learning is a promising tool in the evaluation of haptic skills, further improvement and alignments are required for fully objective and reliable validation in all cases of dental training in restorative dentistry.
dc.identifier.citationOguzhan, A., Peskersoy, C., Devrimci, E. E., Kemaloglu, H., & Onder, T. K. (2025). Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education. Journal of Dental Education, 89(3), 383-397.
dc.identifier.doi10.1002/jdd.13722
dc.identifier.endpage397
dc.identifier.issn00220337
dc.identifier.issue3
dc.identifier.pmid39327675
dc.identifier.scopus2-s2.0-86000435639
dc.identifier.scopusqualityQ2
dc.identifier.startpage383
dc.identifier.urihttps://doi.org/10.1002/jdd.13722
dc.identifier.urihttps://hdl.handle.net/11454/117214
dc.identifier.volume89
dc.identifier.wosWOS:001320673900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorOguzhan, Aybeniz
dc.institutionauthorPeskersoy, Cem
dc.institutionauthorDevrimci, Elif Ercan
dc.institutionauthorKemaloglu, Hande
dc.institutionauthorid0000-0002-4108-2968
dc.institutionauthorid0000-0003-2502-2698
dc.institutionauthorid0000-0003-0957-5348
dc.institutionauthorid0000-0003-0463-373X
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.subjectdeep machine learning
dc.subjectdental education
dc.subjecthaptic skills
dc.subjectreliability
dc.subjectvalidity
dc.titleImplementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education
dc.typeArticle

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