Predictive Modeling for Personalized Three-Dimensional Burn Injury Assessments

Abstract

Abstract For patients with major burn injuries, an accurate burn size estimation is essential to plan appropriate treatment and minimize medical and surgical complications. However, current clinical methods for burn size estimation lack accuracy and reliability. To overcome these limitations, this paper proposes a 3D-based approach—with personalized 3D models from a limited set of anthropometric measurements—to accurately assess the percent TBSA affected by burns. First, a reliability and feasibility study of the anthropometric measuring process was performed to identify clinically relevant measurements. Second, a large representative stratified random sample was generated to output several anthropometric features required for predictive modeling. Machine-learning algorithms assessed the importance and the subsets of anthropometric measurements for predicting the BSA according to specific patient morphological features. Then, the accuracy of both the morphology and BSA of 3D models built from a limited set of measurements was evaluated using error metrics and maximum distances 3D color maps. Results highlighted the height and circumferences of the bust, neck, hips, and waist as the best predictors for BSA. 3D models built from three to four anthropometric measurements showed good accuracy and were geometrically close to gold standard 3D scans. Outcomes of this study aim to decrease medical and surgical complications by decreasing errors in percent TBSA assessments and, therefore, improving patient outcomes by personalizing care.