LAUSR

Patients suffering from pectus excavatum often experience psychosocial distress due to perceived anomalies in their physical appearance. The ability to visually inform patients about their expected aesthetic outcome after surgical correction is still lacking. This study aims to develop an automatic, patient-specific model to predict aesthetic outcome after the Nuss procedure. Patients prospectively received pre-operative and post-operative three-dimensional optical surface scanning of their chest during the Nuss procedure. A prediction model was composed based on nonlinear least squares data-fitting, regression methods and a two-dimensional Gaussian function with adjustable amplitude, variance, rotation, skewness and kurtosis components. Morphological features of pectus excavatum were extracted from pre-operative images using a previously developed surface analysis tool to generate a patient-specific model. Prediction accuracy was evaluated through cross-validation, utilizing the mean root squared deviation and maximum positive and negative deviations as performance measures. The prediction model was evaluated on 30 (90% male) prospectively imaged patients. The model achieved an average root mean squared deviation of 6.3±2.0 mm, with average maximum positive and negative deviations of 12.7±6.1 and -10.2±5.7 mm, respectively, between the predicted and actual post-operative aesthetic result. Our developed two-dimensional Gaussian model based on three-dimensional optical surface images is a clinically promising tool to predict post-surgical aesthetic outcome in patients with pectus excavatum. Prediction of the aesthetic outcome after the Nuss procedure potentially improves information provision and expectation management among patients. Further research should assess whether increasing the sample size may reduce deviations and improve performance.