Chronic wound assessment and infection detection method
Chronic wound assessment and infection detection method
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DOI：
10.1186/s12911-019-0813-0
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发表时间：
2019-05-24
影响因子：
3.5
通讯作者：
Lai, Feipei
中科院分区：
文献类型：
--
作者：
Hsu, Jui-Tse;Chen, Yung-Wei;Lai, Feipei
背景当今许多患者患有慢性伤口和伤口感染。迄今为止，手术后伤口的护理对于医护人员和患者来说仍然是一项繁琐而富有挑战性的工作。因此，在手持移动设备的帮助下，对伤口感染早期检测和伤口自我监测的一系列算法和相关方法的开发有很高的需求。方法本研究提出了一种自动化方法来执行（1）伤口图像分割和（2）手术后伤口感染评估。第一部分描述了一种基于边缘的自适应阈值检测图像分割方法，以从原始图像中排除非受伤区域。第二部分描述了一种基于机器学习方法的伤口感染评估方法。该方法介绍了缝合线区域特征点的提取以及基于单峰Rosin阈值算法的特征点聚类的最优聚类方法。然后将这些簇合并到几个感兴趣区域 (ROI)，每个感兴趣区域都被视为缝合位点。值得注意的是，支持向量机 (SVM) 可以自动解释这些检测到的缝合部位上的感染。结果对于 (1) 伤口图像分割，基于边界的评估应用于 100 张图像，并由三位医生建立了黄金标准。总体而言，其真阳性率达到 76.44%，准确率达到 89.04%。对于 (2) 伤口感染评估，使用三位医生确认的伤口图片进行回顾性研究，得出以下四种症状的结果：(1) 肿胀，(2) 肉芽，(3) 感染，和 (4) 组织坏死。通过对 134 张伤口图像进行交叉验证，对于异常检测，我们的分类器达到了 87.31% 的准确率值；对于症状评估，我们的分类器达到了 83.58% 的准确率。结论这种增强机制已被证明足够可靠，可以减少面对面诊断的需要。为了方便使用这种方法和分析框架，开发了自动伤口解释应用程序和随附的网站。
BackgroundNumerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring.MethodsThis research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site.ResultsFor (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value.ConclusionsThis augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed.