Text-Guided Inpainting for Aesthetic Prediction in Orthofacial Surgery: A Patient-Centered AI Approach.
Vincenzo Abbate, Emanuele Carraturo, Domenico Benfenati, Sara Tramontano, Luigi Angelo Vaira, Stefania Troise, Gabriele Canzi, Giorgio Novelli, Daniela Pacella, Cristiano Russo, Cristian Tommasino, Riccardo Nocini, Antonio M Rinaldi, Giovanni Dell'Aversana Orabona
Abstract
Open AccessBackground and Objectives: Artificial intelligence (AI) is increasingly impacting medicine by improving healthcare delivery and simplifying diagnostic and therapeutic processes. Text-guided inpainting is a promising tool in orthofacial surgery for generating ideal, patient-specific facial profiles. Materials and Methods: A total of 89 patients with dentofacial deformities (DFDs) were evaluated. The DALL-E2 platform was used to generate profilometric transformations based on textual prompts. The resulting images were assessed by three groups: patients, expert surgeons, and the general population. Results: A total of 94% of surgeons, 85% of the general population, and 79% of patients rated the AI-modified profiles as more aesthetically pleasing than the originals. The prompt inspired by runway models had the highest agreement across groups. Conclusions: Generative AI and text-guided inpainting show potential for enhancing aesthetic planning in orthofacial surgery, offering personalized treatment paths and aiding virtual surgical planning.