Machine learning algorithms enhance the accuracy of radiographic diagnosis of dental caries: a comparative study.

ABSTRACT:

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 cognitiv

SUBMITTER: Hegde S 

PROVIDER: S-EPMC12653770 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature