Sarah Rampf, Holger Gehrig, Andreas Möltner, Martin R. Fischer, Falk Schwendicke, Karin C. Huth
{"title":"使用各种反馈方法并结合人工智能应用提高牙科学生的放射诊断能力--随机临床试验。","authors":"Sarah Rampf, Holger Gehrig, Andreas Möltner, Martin R. Fischer, Falk Schwendicke, Karin C. Huth","doi":"10.1111/eje.13028","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Materials and Methods</h3>\n \n <p>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 <i>t</i>-test.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Among 55 students, the eF group by large performed significantly better than the KOR group in detecting enamel caries (accuracy 0.840 ± 0.041, <i>p</i> = .196; sensitivity 0.638 ± 0.204, <i>p</i> = .037; specificity 0.859 ± 0.050, <i>p</i> = .410; ROC AUC 0.748 ± 0.094, <i>p</i> = .020), apical periodontitis (accuracy 0.813 ± 0.095, <i>p</i> = .011; sensitivity 0.476 ± 0.230, <i>p</i> = .003; specificity 0.914 ± 0.108, <i>p</i> = .292; ROC AUC 0.695 ± 0.123, <i>p</i> = .001) and in assessing the image quality of periapical images (<i>p</i> = .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).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":50488,"journal":{"name":"European Journal of Dental Education","volume":"28 4","pages":"925-937"},"PeriodicalIF":1.7000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eje.13028","citationCount":"0","resultStr":"{\"title\":\"Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application—A randomized clinical trial\",\"authors\":\"Sarah Rampf, Holger Gehrig, Andreas Möltner, Martin R. Fischer, Falk Schwendicke, Karin C. Huth\",\"doi\":\"10.1111/eje.13028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Materials and Methods</h3>\\n \\n <p>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 <i>t</i>-test.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Among 55 students, the eF group by large performed significantly better than the KOR group in detecting enamel caries (accuracy 0.840 ± 0.041, <i>p</i> = .196; sensitivity 0.638 ± 0.204, <i>p</i> = .037; specificity 0.859 ± 0.050, <i>p</i> = .410; ROC AUC 0.748 ± 0.094, <i>p</i> = .020), apical periodontitis (accuracy 0.813 ± 0.095, <i>p</i> = .011; sensitivity 0.476 ± 0.230, <i>p</i> = .003; specificity 0.914 ± 0.108, <i>p</i> = .292; ROC AUC 0.695 ± 0.123, <i>p</i> = .001) and in assessing the image quality of periapical images (<i>p</i> = .031). No significant differences were observed for the other outcomes. 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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 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.
期刊介绍：
The aim of the European Journal of Dental Education is to publish original topical and review articles of the highest quality in the field of Dental Education. The Journal seeks to disseminate widely the latest information on curriculum development teaching methodologies assessment techniques and quality assurance in the fields of dental undergraduate and postgraduate education and dental auxiliary personnel training. The scope includes the dental educational aspects of the basic medical sciences the behavioural sciences the interface with medical education information technology and distance learning and educational audit. Papers embodying the results of high-quality educational research of relevance to dentistry are particularly encouraged as are evidence-based reports of novel and established educational programmes and their outcomes.
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