Quantitative and Qualitative Evaluation of a Confidence-Aware Transformer-Based Super-Resolution Framework for Panoramic Radiographs

This study aimed to develop and evaluate a confidence-aware transformer-based super-resolution framework, termed CAT-PRSR, to enhance image quality and diagnostic reliability in panoramic dental radiographs. A total of 1078 anonymised panoramic radiographs were retrospectively collected (950 for training, 128 for testing). The CAT-PRSR framework integrating a transformer-based SR backbone with a confidence-aware training strategy was developed. The model generates a high-resolution output with pixel-wise uncertainty estimation, allowing adaptive learning focused on diagnostically relevant regions while minimising over-enhancement in noise-sensitive areas. Model performance was evaluated using 6 quantitative metrics – peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), spatial correlation coefficient (SCC), natural image quality evaluator (NIQE), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID)—and mean opinion score (MOS) assessment. Based on quantitative performance, 4 representative state-of-the-art SR models were selected for comparison at 4×, 6×, and 8× magnifications. CAT-PRSR demonstrated superior performance across all metrics and magnification levels. It achieved the highest peak signal-to-noise ratio (36.41 at 4 ×, 36.19 at 6 ×, and 33.73 at 8 ×) and the lowest FID (1.77, 9.29, and 2.09, respectively), outperforming all comparison models. In MOS evaluations, CAT-PRSR maintained diagnostic utility scores statistically comparable to ground truth images ( P > .05), while other models showed significant degradation ( P < .001). The proposed CAT-PRSR framework demonstrated potential to enhance panoramic radiograph resolution by integrating pixel-level fidelity with improved diagnostic reliability. The CAT-PRSR model may enhance the diagnostic reliability of panoramic radiographs acquired under low-resolution conditions, supporting more accurate clinical decision-making and serving as a reliable imaging resource for AI-driven dental research.