Comparison of 3D and 2D area measurement of acute burn wounds with LiDAR technique and deep learning model.

Abstract

It is generally understood that wound areas appear smaller when calculated using 2D images, but the factors contributing to this discrepancy are not well-defined. With the rise of 3D photography, 3D segmentation, and 3D measurement, more accurate assessments have become possible. We developed an application called the Burn Evaluation Network (B.E.N.), which combines a deep learning model with LiDAR technology to perform both 2D and 3D measurements. In the first part of our study, we used burn wound templates to verify that the results of 3D segmentation closely matched the actual size of the burn wound and to examine the effect of limb curvature on the 3D/2D area ratio. Our findings revealed that smaller curvatures, indicative of flatter surfaces, were associated with lower 3D/2D area ratios, and larger curvatures corresponded to higher ratios. For instance, the back had the lowest average curvature (0.027 ± 0.004) and the smallest 3D/2D area ratio (1.005 ± 0.055). In the second part of our study, we applied our app to real patients, measuring burn areas in both 3D and 2D. Regions such as the head and neck (ratio: 1.641) and dorsal foot (ratio: 1.908) exhibited significantly higher 3D/2D area ratios. Additionally, images containing multiple burn wounds also showed a larger ratio (1.656) and greater variability in distribution. These findings suggest that 2D segmentation tends to significantly underestimate surface areas in highly curved regions or when measurements require summing multiple wound areas. We recommend using 3D measurements for wounds located on areas like the head, neck, and dorsal foot, as well as for cases involving multiple wounds or large areas, to improve measurement accuracy.