Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan; Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Burns. 2023 Aug;49(5):1039-1051. doi: 10.1016/j.burns.2022.07.006. Epub 2022 Jul 18.
Accurate assessment of the percentage of total body surface area (%TBSA) burned is crucial in managing burn injuries. It is difficult to estimate the size of an irregular shape by inspection. Many articles reported the discrepancy of estimating %TBSA burned by different doctors. We set up a system with multiple deep learning (DL) models for %TBSA estimation, as well as the segmentation of possibly poor-perfused deep burn regions from the entire wound.
We proposed boundary-based labeling for datasets of total burn wound and palm, whereas region-based labeling for the dataset of deep burn wound. Several powerful DL models (U-Net, PSPNet, DeeplabV3+, Mask R-CNN) with encoders ResNet101 had been trained and tested from the above datasets. With the subject distances, the %TBSA burned could be calculated by the segmentation of total burn wound area with respect to the palm size. The percentage of deep burn area could be obtained from the segmentation of deep burn area from the entire wound.
A total of 4991 images of early burn wounds and 1050 images of palms were boundary-based labeled. 1565 out of 4994 images with deep burn were preprocessed with superpixel segmentation into small regions before labeling. DeeplabV3+ had slightly better performance in three tasks with precision: 0.90767, recall: 0.90065 for total burn wound segmentation; precision: 0.98987, recall: 0.99036 for palm segmentation; and precision: 0.90152, recall: 0.90219 for deep burn segmentation.
Combining the segmentation results and clinical data, %TBSA burned, the volume of fluid for resuscitation, and the percentage of deep burn area can be automatically diagnosed by DL models with a pixel-to-pixel method. Artificial intelligence provides consistent, accurate and rapid assessments of burn wounds.
准确评估体表面积烧伤百分比（%TBSA）对于烧伤管理至关重要。通过肉眼观察来估算不规则形状的烧伤面积是很困难的。许多文献报道了不同医生估算%TBSA烧伤的差异。我们建立了一个多深度学习（DL）模型系统，用于%TBSA 估算，以及从整个伤口中分割可能灌注不良的深度烧伤区域。
我们提出了基于边界的全烧伤伤口和手掌数据集的标记方法，而深度烧伤伤口数据集则采用基于区域的标记方法。从上述数据集训练和测试了几种强大的 DL 模型（U-Net、PSPNet、DeeplabV3+、Mask R-CNN），它们的编码器均为 ResNet101。通过受试者距离，可根据手掌大小对全烧伤面积的分割来计算%TBSA 烧伤，从整个伤口中分割深度烧伤区域，可获得深度烧伤面积的百分比。
共对 4991 张早期烧伤伤口图像和 1050 张手掌图像进行了基于边界的标记。对 1565 张有深度烧伤的图像，在进行标记前，使用超像素分割将其预处理成小区域。在全烧伤伤口分割、手掌分割和深度烧伤分割三个任务中，DeeplabV3+ 的精度分别为 0.90767、0.98987 和 0.90152，召回率分别为 0.90065、0.99036 和 0.90219，表现略优。
通过结合分割结果和临床数据，DL 模型可以通过像素到像素的方法自动诊断%TBSA 烧伤、复苏所需的液体量和深度烧伤面积。人工智能为烧伤伤口提供了一致、准确和快速的评估。
