Predictive Modeling for Personalized Three-Dimensional Burn Injury Assessments.

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.

The Importance of a Three-dimensional-Based Approach With Personalized Models for Accurately Assessing TBSA.

In this article, the relevance of a new three-dimensional computer-Based computer-based framework with personalized 3D models for accurately assessing the TBSA is demonstrated through preliminary results and validation studies. First, a 3D rendering interface was developed for representation and calculation of TBSA. The personalized 3D models were built from anthropometric measurements using MakeHuman software. Fifteen paired models were randomly built with an equal BSA but a different morphology. The difference of local BSA, TBSA burned, and Parkland fluids between each paired models were observed to highlight the impact of morphology's variation on the TBSA. Finally, a preliminary validation study was made on 4 mannequins by 14 volunteers to assess the accuracy of the 3D models built with MakeHuman software and TBSA burned assessment with the proposed method. Small variations in the morphology impacted the TBSA assessment. Mannequin's 3D models built with MakeHuman software presented an absolute error of 3 ± 2.2 % with no significant difference with their scans. The proposed approach allows for a better assessment of TBSA with a lower variability. No significant difference in the scores for expert and nonexpert conditions was observed. Personalized 3D model to the patient's morphology is suggested to overcome the difficulty of patients with specific morphologies such as obese and children. The proposed framework appears to be relevant for personalizing and accurately assessing TBSA and could reduce morbidity and mortality.