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Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application-A randomized clinical trial.
Rampf, Sarah; Gehrig, Holger; Möltner, Andreas; Fischer, Martin R; Schwendicke, Falk; Huth, Karin C.
Afiliación
  • 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.
Eur J Dent Educ ; 28(4): 925-937, 2024 Nov.
Article en En | MEDLINE | ID: mdl-39082447
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estudiantes de Odontología / Inteligencia Artificial / Radiografía Dental / Competencia Clínica / Caries Dental / Educación en Odontología Límite: Female / Humans / Male Idioma: En Revista: Eur J Dent Educ Asunto de la revista: EDUCACAO / ODONTOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estudiantes de Odontología / Inteligencia Artificial / Radiografía Dental / Competencia Clínica / Caries Dental / Educación en Odontología Límite: Female / Humans / Male Idioma: En Revista: Eur J Dent Educ Asunto de la revista: EDUCACAO / ODONTOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido