Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application-A randomized clinical trial.

Autor: Rampf S; Department of Conservative Dentistry, University Hospital Heidelberg, Heidelberg University, Heidelberg, Germany., Gehrig H; Department of Conservative Dentistry, University Hospital Heidelberg, Heidelberg University, Heidelberg, Germany., Möltner A; Deans Office of the Medical Faculty, Heidelberg University, Heidelberg, Germany., Fischer MR; Institute of Medical Education, LMU University Hospital, LMU Munich, Munich, Germany., Schwendicke F; Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany., Huth KC; Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany.
Jazyk: angličtina
Zdroj: European journal of dental education : official journal of the Association for Dental Education in Europe [Eur J Dent Educ] 2024 Nov; Vol. 28 (4), pp. 925-937. Date of Electronic Publication: 2024 Jul 31.
DOI: 10.1111/eje.13028
Abstrakt: Introduction: Radiographic diagnostic competences are a primary focus of dental education. This study assessed two feedback methods to enhance learning outcomes and explored the feasibility of artificial intelligence (AI) to support education.
Materials and Methods: Fourth-year dental students had access to 16 virtual radiological example cases for 8 weeks. They were randomly assigned to either elaborated feedback (eF) or knowledge of results feedback (KOR) based on expert consensus. Students´ diagnostic competences were tested on bitewing/periapical radiographs for detection of caries, apical periodontitis, accuracy for all radiological findings and image quality. We additionally assessed the accuracy of an AI system (dentalXrai Pro 3.0), where applicable. Data were analysed descriptively and using ROC analysis (accuracy, sensitivity, specificity, AUC). Groups were compared with Welch's t-test.
Results: Among 55 students, the eF group by large performed significantly better than the KOR group in detecting enamel caries (accuracy 0.840 ± 0.041, p = .196; sensitivity 0.638 ± 0.204, p = .037; specificity 0.859 ± 0.050, p = .410; ROC AUC 0.748 ± 0.094, p = .020), apical periodontitis (accuracy 0.813 ± 0.095, p = .011; sensitivity 0.476 ± 0.230, p = .003; specificity 0.914 ± 0.108, p = .292; ROC AUC 0.695 ± 0.123, p = .001) and in assessing the image quality of periapical images (p = .031). No significant differences were observed for the other outcomes. The AI showed almost perfect diagnostic performance (enamel caries: accuracy 0.964, sensitivity 0.857, specificity 0.074; dentin caries: accuracy 0.988, sensitivity 0.941, specificity 1.0; overall: accuracy 0.976, sensitivity 0.958, specificity 0.983).
Conclusion: Elaborated feedback can improve student's radiographic diagnostic competences, particularly in detecting enamel caries and apical periodontitis. Using an AI may constitute an alternative to expert labelling of radiographs.
(© 2024 The Author(s). European Journal of Dental Education published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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