Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Purpose 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.
Açıklama
Anahtar Kelimeler
deep machine learning, dental education, haptic skills, reliability, validity
Kaynak
Journal of Dental Education
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
89
Sayı
3
Künye
Oguzhan, 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.
