Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application: a randomized clinical trial
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 acc...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
November 2024
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| In: |
European journal of dental education
Year: 2024, Volume: 28, Issue: 4, Pages: 925-937 |
| ISSN: | 1600-0579 |
| DOI: | 10.1111/eje.13028 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/eje.13028 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/eje.13028 
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| Author Notes: | Sarah Rampf, Holger Gehrig, Andreas Möltner, Martin R. Fischer, Falk Schwendicke, Karin C. Huth |
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| 520 | |a 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. | ||
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a dental education | |
| 650 | 4 | |a diagnostic competences | |
| 650 | 4 | |a feedback | |
| 650 | 4 | |a radiographic diagnostics | |
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