Cheiloplasty Pre-Planning through Machine Learning: A Conceptual Approach

This paper introduces a proof-of-concept study focused on AI-powered pre-surgery planning for cheiloplasty, a vital aspect of plastic surgery dedicated to enhancing and reshaping the lips. The research tackles the intricate task of selecting the most suitable surgical approach by harnessing the capabilities of artificial intelligence (AI) and machine learning. The methodology entails a comprehensive analysis and the extraction of features from various facial components, including the lips, nose, eyes, and eyebrows alongside an assessment of overall facial aesthetics. Utilizing advanced computer vision algorithms, these facial components are extracted from preoperative images. Central to the system is a Multi-Modal Convolutional Neural Network (CNN) architecture, which leverages the extracted facial features to predict outcomes for specific cheiloplasty techniques. Rigorous training and validation of the CNN involve meticulous comparisons with ground truth data and expert assessments to ensure precision and dependability. Furthermore, the study underscores the system’s user-friendliness and practicality by gathering feedback from seasoned plastic surgeons. Adherence to strict ethical guidelines guarantees the protection of patient data and the mitigation of potential biases. The successful development of this proof of concept highlights the vast potential of AI integration in pre-surgery planning for cheiloplasty. By equipping surgeons with technique-specific insights derived from facial components and style analysis, the system enhances decision-making, ultimately benefiting patient care and treatment outcomes. Future endeavours will expand the dataset, consider additional variables, and encompass prospective clinical trials to validate the system’s real-world impact in the field of lip-repair surgery.