Czajkowska Joanna, Polańska Adriana, Slian Anna, Dańczak-Pazdrowska Aleksandra
Faculty of Biomedical Engineering, Silesian University of Technology, 41-800, Zabrze, Poland.
Department of Dermatology and Venereology, Poznan University of Medical Sciences, Poznan, Poland.
Sci Rep. 2025 Jan 2;15(1):163. doi: 10.1038/s41598-024-84051-6.
The last decades have brought an interest in ultrasound applications in dermatology. Especially in the case of atopic dermatitis, where the formation of a subepidermal low echogenic band (SLEB) may serve as an independent indicator of the effects of treatment, the use of ultrasound is of particular interest. This study proposes and evaluates the computer-aided diagnosis method for assessing atopic dermatitis (AD). The fully automated image processing framework combines advanced machine learning techniques for fast, reliable, and repeatable HFUS image analysis, supporting clinical decisions. The proposed methodology comprises accurate SLEB segmentation followed by a classification step. The data set includes 20 MHz images of 80 patients diagnosed with AD according to Hanifin and Rajka criteria, which were evaluated before and after treatment. The ground true labels- clinical evaluation based on Investigator Global Assessment index (IGA score) together with ultrasound skin examination was performed. For reliable analysis, in further experiments, two experts annotated the HFUS images twice in two-week intervals. The analysis aimed to verify whether the fully automated method can classify the HFUS images at the expert level. The Dice coefficient values for segmentation reached 0.908 for SLEB and 0.936 for the entry echo layer. The accuracy of SLEB presence detection results (IGA0) is equal to 98% and slightly outperforms the experts' assessment, which reaches 96%. The overall accuracy of the AD assessment was equal to 69% (Cohen's kappa 0.78) and was comparable with the experts' assessment, ranging between 64% and 70% (Cohen's kappa 0.73-0.79). The results indicate that the automated method can be applied to AD assessment, and its combination with standard diagnosis may benefit repeatable analysis and a better understanding of the processes that take place within the skin and aid treatment monitoring.
在过去几十年里，超声技术在皮肤科的应用受到了广泛关注。特别是在特应性皮炎的情况下，表皮下低回声带（SLEB）的形成可作为治疗效果的独立指标，超声的应用尤其受到关注。本研究提出并评估了一种用于评估特应性皮炎（AD）的计算机辅助诊断方法。这个全自动图像处理框架结合了先进的机器学习技术，可实现快速、可靠且可重复的高频超声（HFUS）图像分析，为临床决策提供支持。所提出的方法包括精确的SLEB分割，随后是一个分类步骤。数据集包含80例根据Hanifin和Rajka标准诊断为AD的患者的20 MHz图像，这些图像在治疗前后均进行了评估。基于研究者整体评估指数（IGA评分）以及超声皮肤检查进行了临床评估的真实标签。为了进行可靠的分析，在进一步的实验中，两位专家每隔两周对HFUS图像进行两次标注。该分析旨在验证全自动方法是否能够在专家水平上对HFUS图像进行分类。SLEB分割的Dice系数值达到0.908，进入回声层的Dice系数值达到0.936。SLEB存在检测结果（IGA0）的准确率为98%，略高于专家评估的96%。AD评估的总体准确率为69%（Cohen's kappa 0.78），与专家评估相当，专家评估的准确率在64%至70%之间（Cohen's kappa 0.73 - 0.79）。结果表明，该自动化方法可应用于AD评估，其与标准诊断相结合可能有助于进行可重复分析，更好地理解皮肤内发生的过程，并辅助治疗监测。
