Cone-beam computed tomography (CBCT) plays an important role in dental diagnosis; however, radiation exposure remains a concern. This study evaluated the feasibility of artificial intelligence (AI)-based image processing for improving image quality in low-dose CBCT. CBCT scans were acquired from a single healthy adult male at three radiation dose levels (10%, 20%, and 100% of the standard dose), and each dataset was subsequently processed using an AI-based image enhancement model. Five dental specialists independently assessed image quality using a 6-point scoring system across 12 anatomical and diagnostic criteria, including anatomical visibility, structural delineation, and overall diagnostic acceptability. The AI-processed 20% dose images showed no statistically significant difference in image quality compared with the 100% raw dose images (median 4.45, range 3.50–5.30 vs. median 5.05, range 4.50–5.50; p > 0.05). In contrast, the AI-processed 10% dose images demonstrated significantly lower scores (p = 0.0074), and the AI-processed 100% dose images were rated lower than the corresponding raw images. These preliminary findings suggest that AI-assisted enhancement may partially mitigate image quality degradation associated with moderate CBCT dose reduction. Further large-scale studies involving diverse patient populations and clinical settings are required to validate these results.
Park et al. (Thu,) studied this question.