Sakakibara Shunsuke, Takekawa Akira, Takekawa Chikara, Nagai Satoshi, Terashi Hiroto
Department of Plastic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, Japan.
Graduate School of Human Development and Environment, Kobe University, Kobe 657-8501, Japan.
J Clin Med. 2023 Mar 12;12(6):2194. doi: 10.3390/jcm12062194.
Artificial intelligence (AI) in medical care can raise diagnosis accuracy and improve its uniformity. This study developed a diagnostic imaging system for chronic wounds that can be used in medically underpopulated areas. The image identification algorithm searches for patterns and makes decisions based on information obtained from pixels rather than images. Images of 50 patients with pressure sores treated at Kobe University Hospital were examined. The algorithm determined the presence of necrosis with a significant difference ( = 3.39 × 10). A threshold value was created with a luminance difference of 50 for the group with necrosis of 5% or more black pixels. In the no-necrosis group with less than 5% black pixels, the threshold value was created with a brightness difference of 100. The "shallow wounds" were distributed below 100, whereas the "deep wounds" were distributed above 100. When the algorithm was applied to 24 images of 23 new cases, there was 100% agreement between the specialist and the algorithm regarding the presence of necrotic tissue and wound depth evaluation. The algorithm identifies the necrotic tissue and wound depth without requiring a large amount of data, making it suitable for application to future AI diagnosis systems for chronic wounds.
医疗保健中的人工智能（AI）可以提高诊断准确性并改善其一致性。本研究开发了一种用于慢性伤口的诊断成像系统，可用于医疗资源匮乏地区。图像识别算法基于从像素而非图像中获取的信息来搜索模式并做出决策。对在神户大学医院接受治疗的50例压疮患者的图像进行了检查。该算法确定坏死的存在具有显著差异（= 3.39×10）。对于黑色像素坏死率为5%或更高的组，以50的亮度差异创建阈值。对于黑色像素少于5%的无坏死组，以100的亮度差异创建阈值。“浅伤口”分布在100以下，而“深伤口”分布在100以上。当该算法应用于23例新病例的24张图像时，专家与算法在坏死组织的存在和伤口深度评估方面达成了100%的一致。该算法无需大量数据即可识别坏死组织和伤口深度，使其适用于未来慢性伤口的人工智能诊断系统。
