Pre-planning for Plastic Surgery Using Machine Learning: A Proof of Concept

Abstract

This paper presents a proof-of-concept study on AI-based pre-surgery planning in plastic surgery. The study addresses the challenge of technique selection by developing an AI-driven system that utilises machine learning algorithms to analyse patient-specific data and historical outcomes. By comparing and evaluating diverse inputs, the system generates detailed results for each technique, providing surgeons with valuable insights into expected outcomes. This enhances decision-making during pre-surgery planning and improves surgical precision. The system’s development involved addressing challenges related to data availability, algorithm selection, and interpretability. Preoperative images will be processed using advanced computer vision algorithms to extract relevant features. A Convolutional Neural Network (CNN) architecture predicted technique-specific outcomes based on the extracted features. The validation included comparing predictions against ground truth data and expert evaluations. Feedback from plastic surgery practitioners will be collected to assess usability and practicality. Ethical guidelines will be strictly followed to ensure patient data protection and address potential biases. The successful implementation of the proof of concept demonstrates the potential of AI integration in pre-surgery planning for plastic surgery. By empowering surgeons with technique-specific insights, the system enhances decision-making, ultimately improving patient care and treatment outcomes. Future work involves expanding the dataset, considering additional variables, and conducting prospective clinical trials to validate the system’s real-world impact.