Evaluation of Modified AI-Enhanced Radiographic Images of Artificial Teeth for Caries Removal Decision-Making in Predoctoral Dental Students

Abstract

Background: Accurate radiographic interpretation is essential for caries removal decision-making but remains challenging for early dental learners. Artificial intelligence (AI) has demonstrated promise in improving caries detection; however, its role in supporting operative decision-making during preclinical training remains unclear.

Objective: To evaluate the effect of modified AI-enhanced radiographic images on the quality of caries removal performed by first-year dental students and to determine whether the effect of modified AI-enhanced images differs between shallow and deeper lesions.

Methods: A two-period crossover study was conducted involving first-year dental students in a preclinical operative dentistry course. Participants performed caries removal on standardized 3D-printed teeth containing either shallow or deeper carious lesions. Students completed procedures using either standard bitewing radiographs or modified AI-enhanced radiographic images. Caries removal quality was assessed using the Composite Caries Removal Quality Score (CRQS), which incorporated convenience form, caries removal at the dentinoenamel junction (DEJ), caries removal at the pulpal floor. Completion time was also recorded.

Results: Modified AI-enhanced radiographic images significantly improved overall CRQS compared with standard radiographs (p = 0.002). This improvement was primarily observed in shallow lesions, which demonstrated significantly higher CRQS scores under the modified AI-enhanced condition (p = 0.001), whereas no significant difference was found for deeper lesions (p = 0.727). The greatest improvement was observed in convenience form for shallow lesions (p < 0.001). No significant differences were detected for caries removal at the DEJ or pulpal floor. Lesion depth significantly influenced several outcomes, with deeper lesions demonstrating lower DEJ scores and requiring longer completion times. Modified AI-enhanced images did not significantly affect completion time.

Conclusions: Modified AI-enhanced radiographic images improved the quality of caries removal performed by first-year dental students, particularly for shallow lesions where radiographic interpretation is more challenging. The benefits were primarily related to improved convenience form rather than caries removal at the DEJ or pulpal floor. These findings suggest that modified AI-enhanced radiographic images may be a valuable adjunct in preclinical dental education and may support the development of diagnostic and operative decision-making skills in early learners.