Dento maxillo facial radiology · 2025
Machine learning algorithms enhance the accuracy of radiographic diagnosis of dental caries: a comparative study.
СтатьяСравнительное исследованиеРандомизированное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Hegde Shwetha, Gao Jinlong, Cox Stephen, Nanayakkara Shanika, Logothetis Rena, Vasa Rajesh
Аннотация
OBJECTIVES: This study evaluated the influence of cognitive aids, including machine learning (ML) algorithms and checklists, on the diagnostic accuracy and confidence of dental students in detecting dental caries on bitewing radiographs.
METHODS: Fifty-two third-year dental students were randomly assigned to control, ML, or checklist groups. The participants recorded their caries diagnoses (charting) on 10 bitewing radiographs and rated their confidence. Diagnostic accuracy and reliability were compared between groups for caries detection (present/absent). The inter-rater reliability for International Caries Detection and Assessment System II (ICDAS II) caries grading was assessed using weighted kappa. Participants also completed questionnaires on their perceptions of cognitive aids.
RESULTS: ML group showed the highest diagnostic accuracy and confidence levels. For caries detection, the ML group achieved the highest sensitivity (79%) and diagnostic odds ratio (20.3), while the checklist group had the highest specificity (90.9%) (P < .001). The control group showed moderate sensitivity (67.9%) but outperformed the checklist group in this metric. Inter-rater agreement for caries detection was highest in the ML group (κ = 0.702, 95% CI: 0.692-0.713; P < .001), followed by the checklist group. The ML group also had the highest weighted kappa for ICDAS II grading (κ = 0.520, P < .001). Confidence levels differed significantly between groups (P < .001), with the ML group reporting the highest confidence.
CONCLUSION: ML algorithms may enhance diagnostic accuracy and confidence, possibly by reducing cognitive load. While standardizing the diagnostic process, checklists were less effective, likely due to their lack of flexibility and clinical context. Further research is needed to better understand our findings and translate them into clinical workflows.
METHODS: Fifty-two third-year dental students were randomly assigned to control, ML, or checklist groups. The participants recorded their caries diagnoses (charting) on 10 bitewing radiographs and rated their confidence. Diagnostic accuracy and reliability were compared between groups for caries detection (present/absent). The inter-rater reliability for International Caries Detection and Assessment System II (ICDAS II) caries grading was assessed using weighted kappa. Participants also completed questionnaires on their perceptions of cognitive aids.
RESULTS: ML group showed the highest diagnostic accuracy and confidence levels. For caries detection, the ML group achieved the highest sensitivity (79%) and diagnostic odds ratio (20.3), while the checklist group had the highest specificity (90.9%) (P < .001). The control group showed moderate sensitivity (67.9%) but outperformed the checklist group in this metric. Inter-rater agreement for caries detection was highest in the ML group (κ = 0.702, 95% CI: 0.692-0.713; P < .001), followed by the checklist group. The ML group also had the highest weighted kappa for ICDAS II grading (κ = 0.520, P < .001). Confidence levels differed significantly between groups (P < .001), with the ML group reporting the highest confidence.
CONCLUSION: ML algorithms may enhance diagnostic accuracy and confidence, possibly by reducing cognitive load. While standardizing the diagnostic process, checklists were less effective, likely due to their lack of flexibility and clinical context. Further research is needed to better understand our findings and translate them into clinical workflows.