Deep Learning Framework for Facial Reconstruction Outcome Prediction: Integrating Image Inpainting and Depth Estimation for Computer-Assisted Surgical Planning

Facial reconstructive surgery requires precise preoperative planning to optimize functional and aesthetic outcomes, but current imaging technologies like CT and MRI do not offer visualization of expected post-surgical appearance, limiting surgical planning capabilities. We developed a deep learning framework integrating facial inpainting and monocular depth estimation models to predict surgical outcomes and enable 2D and 3D planning from clinical photographs. Three state-of-the-art inpainting architectures (LaMa, LGNet, MAT) and three monocular depth estimation approaches (ZoeDepth, Depth Anything V2, DepthPro) were evaluated using the FFHQ dataset for inpainting and C3I-SynFace dataset for depth estimation, with comprehensive quantitative metrics assessing reconstruction quality and depth accuracy. For anatomically specific facial features, LGNet demonstrated superior performance across eyebrows (PSNR: 25.11, SSIM: 0.75), eyes (PSNR: 20.08, SSIM: 0.53), nose (PSNR: 25.70, SSIM: 0.88), and mouth (PSNR: 22.39, SSIM: 0.75), with statistically significant differences confirmed by paired t-tests (p < 0.001) and large effect sizes (Cohen’s d = 2.25–6.33). DepthPro significantly outperformed competing depth estimation models with absolute relative difference of 0.1426 (78% improvement over Depth Anything V2: 0.6453 and ZoeDepth: 0.6509) and δ1 accuracy of 0.8373 (versus 0.6697 and 0.5271 respectively). This novel framework addresses a critical gap in surgical planning by providing comprehensive preoperative visualization of potential outcomes from standard clinical photographs, supporting applications from maxillofacial reconstruction to orbital and nasal procedures.