Abstract：
The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera. Subsequently, we assessed the deep learning model\'s performance by comparing the results with ground-truth data. Using contrast-limited adaptive histogram equalization (CLAHE), an image preprocessing technique designed to enhance image contrast, we augmented the chromatic contrast in 46 SPT images from 33 participants. We established a deep learning model for wheal and erythema segmentation using 144 and 150 training datasets, respectively. The wheal segmentation model achieved an accuracy of 0.9985, a sensitivity of 0.5621, a specificity of 0.9995, and a Dice similarity coefficient of 0.7079, whereas the erythema segmentation model achieved an accuracy of 0.9660, a sensitivity of 0.5787, a specificity of 0.97977, and a Dice similarity coefficient of 0.6636. The use of image preprocessing and deep learning technology in SPT is expected to have a significant positive impact on medical practice by ensuring the accurate segmentation of wheals and erythema, producing consistent evaluation results, and simplifying diagnostic processes.
摘要：
皮肤点刺试验（SPT）是识别与免疫球蛋白E介导的过敏性疾病（如哮喘）相关的致敏过敏原的关键工具。过敏性鼻炎,特应性皮炎,荨麻疹,血管性水肿,和过敏反应。然而,由于需要测量皮肤上过敏原引起的红斑和风团的大小,因此SPT是劳动密集型且耗时的。在这项研究中,我们使用图像预处理方法和深度学习模型对智能手机摄像头拍摄的SPT图像中的风尚和红斑进行了分割.随后，我们通过将结果与真实数据进行比较来评估深度学习模型的性能。使用对比度限制的自适应直方图均衡(CLAHE)，一种旨在增强图像对比度的图像预处理技术，我们增强了来自33名参与者的46张SPT图像中的色度对比度。我们使用144和150个训练数据集建立了用于风疹和红斑分割的深度学习模型，分别。风团分割模型的准确度为0.9985，灵敏度为0.5621，特异性为0.9995，Dice相似系数为0.7079，而红斑分割模型的准确度为0.9660，灵敏度为0.5787，特异性为0.97977，Dice相似系数为0.6636。在SPT中使用图像预处理和深度学习技术，通过确保对风团和红斑的准确分割，有望对医疗实践产生重大的积极影响。产生一致的评估结果，简化诊断流程。
