Health Informatics Research Group, Osnabrück University of AS, Germany.
Department of Dermatology, Venerology and Allergology, University Hospital of Essen, Germany.
Stud Health Technol Inform. 2022 May 25;294:63-67. doi: 10.3233/SHTI220397.
Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.
静脉性腿部溃疡和糖尿病足溃疡是最常见的慢性伤口。近年来，它们的发病率显著增加，消耗了稀缺的护理资源。本研究旨在探索用于图像中这些类型伤口的检测和分类算法的性能。为此，应用了预训练模型的 YoloV5 系列算法对包含至少一种两种伤口类型的 885 张图像进行分析。YoloV5m6 模型提供了最高的精度（0.942）和高召回率（0.837）。其 mAP_0.5:0.95 为 0.642。虽然后者的值与文献中报告的值相当，但精度和召回率都高得多。总之，我们在良好的伤口检测和分类方面的结果可能为（半自动）将伤口信息输入患者记录开辟了一条道路。为了增强临床医生的信任，我们目前正在整合一个仪表板，临床医生可以根据自己的专业知识来检查预测的有效性。
