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Periodontal disease is a widespread global health concern that necessitates an accurate diagnosis for effective treatment. Traditional diagnostic methods based on panoramic radiographs are often limited by subjective evaluation and low-resolution imaging, leading to suboptimal precision. This study presents an approach that integrates Super-Resolution Generative Adversarial Networks (SRGANs) with deep learning-based segmentation models to enhance the segmentation of periodontal bone loss (PBL) areas on panoramic radiographs. By transforming low-resolution images into high-resolution versions, the proposed method reveals critical anatomical details that are essential for precise diagnostics. The effectiveness of this approach was validated using datasets from the Chungbuk National University Hospital and the Kaggle data portal, demonstrating significant improvements in both image resolution and segmentation accuracy. The SRGAN model, evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, achieved a PSNR of 30.10 dB and an SSIM of 0.878, indicating high fidelity in image reconstruction. When applied to semantic segmentation using a U-Net architecture, the enhanced images resulted in a dice similarity coefficient (DSC) of 0.91 and an intersection over union (IoU) of 84.9%, compared with 0.72 DSC and 65.4% IoU for native low-resolution images. These results underscore the potential of SRGAN-enhanced imaging to improve PBL area segmentation and suggest broader applications in medical imaging, where enhanced image clarity is crucial for diagnostic accuracy. This study also highlights the importance of further research to expand the dataset diversity and incorporate clinical validation to fully realize the benefits of super-resolution techniques in medical diagnostics.

Rice is one of the plants proven to possess antioxidant, anti-inflammatory, anti-proliferative, and whitening properties, making it one of the most beneficial plants in this regard. This study aimed to introduce a novel natural cosmetic and pharmaceutical product based on rice callus as a source of active ingredients that can inhibit skin melanoma cell (B16F10) proliferation and brighten the skin. The 2,4-D hormone at concentrations of 1 µg/mL and 1.5 µg/mL was used to induce rice callus formation. Rice callus extracts were then prepared using aqueous and ethanolic solvents, with a concentration of 1 mg/mL used for characterization tests. To determine the optimal hormone concentration, the phenols/flavonoids, antioxidant activity, proteins, and carbohydrates in the extracts were measured. The optimal concentration of the hormone was found to be 1 µg/mL. Finally, the anti-melanocyte and skin-whitening activity of the extracts was assessed through measurements of their cytotoxicity and inhibition of melanin synthesis-related enzymes. Cellular cytotoxicity measurements revealed that the ethanolic extract induced more cytotoxicity than the aqueous extract, with IC 50 values of 566.3 µg/mL for the ethanolic extract and 1327 µg/mL for the aqueous extract. Skin-whitening-related tests demonstrated that the extracts were 1.7 times more effective than arbutin in inhibiting factors that cause hyperpigmentation. The aqueous extract achieved 85% inhibition of melanin biosynthesis at a concentration of 3200 µg/mL, compared to 68% for the ethanolic extract and 50% for arbutin. Based on these findings, rice callus extract can be introduced as a new, effective substance for skin-lightening and anti-melanocyte products in cosmeceutical and pharmaceutical formulations.