Rejuvenate: Face and Body Retouching Using Image Inpainting on SDG’s
Hossam AbdelRahman, Sama Rostom, Yara Lotfy, Salma Salah Eldeen, Reem Yassein, Nour El-Huda Awny · Journal of Lifestyle and SDGs Review · 2025
Objectives: The main objective of this study is to develop a deep learning-based image inpainting model to assist individuals—particularly those affected by accidents, burns, or genetic conditions—in visualizing potential outcomes of plastic surgery. The goal is to reduce anxiety and emotional distress related to post-surgical appearance by providing realistic image-based previews. Theoretical Framework: The research is grounded in the psychological impact of physical appearance and the growing public interest in cosmetic and reconstructive surgery. It draws on recent advances in deep neural networks, particularly image inpainting, which allows machines to intelligently reconstruct missing or damaged parts of an image. This theoretical basis supports the idea that enhanced visual communication can reduce patient anxiety and improve decision-making in medical contexts. Method: The study proposes a model based on the Stable Diffusion Inpainting technique, which is tailored for medical imaging applications. The system processes input images from patients, analyzes the structure, identifies damaged or missing regions, and reconstructs them using deep learning. The approach leverages advancements in deep generative models to generate realistic and context-aware visual outputs. Results and Discussion: The implemented model demonstrated strong performance in reconstructing affected areas of medical images, offering visually coherent and realistic outcomes. These reconstructions allowed patients to better visualize potential post-surgery results, thereby reducing uncertainty and improving confidence in medical decisions. The study highlights how deep learning can enhance the patient experience through more accurate and empathetic visual communication tools. Research Implications: This technology has implications for both medical and psychological practices. It offers a valuable tool for surgeons to communicate with patients more effectively, and it can support psychological well-being by addressing body image concerns preoperatively. The method may also contribute to broader applications in personalized medicine and digital healthcare interfaces. Originality/Value: The study presents a novel application of Stable Diffusion Inpainting in the medical field, specifically targeting emotional and psychological challenges faced by patients undergoing reconstructive or cosmetic surgery. By combining AI-driven visualization with patient-centered care, the research adds unique value to both technological and healthcare domains, bridging the gap between deep learning innovation and real-world therapeutic impact.