Background/Objectives: The aim of this study is to test different convolutional neural network (CNN) and Transformer-based models to detect and classify vertical misfit at the abutment-prosthesis interface on panoramic radiographs, and to develop a hybrid deep learning model enhanced with attention mechanisms. Methods: A dataset consisting of a total of 566 images, manually classified as 249 ‘fit’ and 317 ‘misfit’ cases by two experts, was created. Images were resized to 224 × 224 and divided into training, validation, and test groups. The deep learning model yielding the most successful results was determined as the backbone; a hybrid model was developed by integrating three different attention modules (SE, CBAM, and ECA) into this structure. Model performance was evaluated using accuracy, precision, sensitivity, and F1 score metrics. Results: CNN-based models (RegNetY-800MF, ConvNeXt-Tiny, EfficientNetV2-S, ResNet50) performed better than Transformer-based models (DeiT, Swin-Tiny) in all metrics. The proposed hybrid model exhibited the highest success among all tested models with a 99.12% accuracy rate. This model reached a 100% precision value in the misfit group and yielded no false positive results. The F1 scores of the hybrid model were recorded as 99.01% for the fit group and 99.21% for the misfit group. Conclusions: The findings of this study demonstrate that attention-enhancing deep learning frameworks have the potential to significantly improve the diagnostic utility of routine panoramic radiographs. It shows that panoramic imaging, when supported by advanced artificial intelligence, can provide valuable diagnostic support in detecting vertical misfit. The developed model has the potential to become a reliable clinical decision support system.

Three-dimensional (3D)-printed permanent crown resins are increasingly used in restorative dentistry, but their residual monomer elution compared with computer-aided design/computer-aided manufacturing (CAD/CAM) materials remains insufficiently characterized. This in vitro study compared residual monomer elution from three permanent crown materials at different storage times. A CAD/CAM hybrid ceramic (GC Cerasmart 270) and two 3D-printed permanent crown resins (Graphy Tera Harz and Saremco CrownTec) were evaluated. Specimens (12 × 14 × 2 mm; n = 5 per material) were stored in 75% ethanol, and the extraction solutions were analyzed by high-performance liquid chromatography (HPLC) on days 1 and 30. The eluted amounts of Bis-GMA, TEGDMA, UDMA, and HEMA were quantified and analyzed using repeated-measures ANOVA, Kruskal–Wallis, and Wilcoxon signed-rank tests (p < 0.05). Material, time, and their interaction were significant for Bis-GMA (p < 0.001). Material differences were also significant for TEGDMA and UDMA (p = 0.002), whereas the time-dependent increases in TEGDMA, UDMA, and HEMA did not reach statistical significance. Graphy showed the highest Bis-GMA and UDMA elution, Saremco the highest TEGDMA and HEMA elution, and Cerasmart the lowest values for all monomers. Both material characteristics and storage time should therefore be considered when evaluating residual monomer elution.