A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
This addresses the need for AI-guided surgical planning in cleft lip repair to improve outcomes for patients, though it appears incremental as it builds on existing inpainting techniques for a specific medical application.
The study tackled the problem of predicting post-surgical appearance for cleft lip patients using AI, proposing a deep learning-based image inpainting method that generated non-cleft lips and noses, with experiments on real-world datasets assessed by expert surgeons to demonstrate feasibility.
A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in improving surgical outcomes. If AI can be used to predict what a repaired cleft lip would look like, surgeons could use it as an adjunct to adjust their surgical technique and improve results. To explore the feasibility of this idea while protecting patient privacy, we propose a deep learning-based image inpainting method that is capable of covering a cleft lip and generating a lip and nose without a cleft. Our experiments are conducted on two real-world cleft lip datasets and are assessed by expert cleft lip surgeons to demonstrate the feasibility of the proposed method.