利用相似聚类机器学习方法进行银屑病严重程度评估，减少观察内和观察间变化
Psoriasis Severity Assessment with a Similarity-Clustering Machine Learning Approach Reduces Intra- and Inter-observation variation
摘要：
银屑病是一种复杂的疾病，在基因型和表型上有许多变异。医学的普遍进步使医生和皮肤科医生的评估和治疗更加复杂。即使我们所有的技术进步，我们仍然主要使用20世纪70年代开发的评估工具Psign Area and Severity Index（PASI）进行严重性评估。在这项研究中，我们评估了一种方法，涉及数字图像，比较网络应用程序和相似性聚类，开发，以提高评估工具的内部和观察员之间的变化。从移动终端收集患者图像。在大约1周后采集相同病变区域的图像。五位皮肤科医生通过改良PASI、绝对评分和使用相似性聚类的相对成对PASI评分评估银屑病的严重程度，并使用一次显示两张图像的网络程序进行。由相同或不同皮肤科医生对单张照片进行mPASI评分，mPASI评分分别为50%至80%。使用相似性聚类的重复mPASI比较显示一致的mPASI评级> 95%。绝对评分和成对评分进展之间的Pearson相关性为0.72。
英文摘要：
Psoriasis is a complex disease with many variations in genotype and phenotype. General advancements in medicine has further complicated both assessments and treatment for both physicians and dermatologist alike. Even with all of our technological progress we still primarily use the assessment tool Psoriasis Area and Severity Index (PASI) for severity assessments which was developed in the 1970s. In this study we evaluate a method involving digital images, a comparison web application and similarity clustering, developed to improve the assessment tool in terms of intra- and inter-observer variation. Images of patients was collected from a mobile device. Images were captured of the same lesion area taken approximately 1 week apart. Five dermatologists evaluated the severity of psoriasis by modified-PASI, absolute scoring and a relative pairwise PASI scoring using similarity-clustering and conducted using a web-program displaying two images at a time. mPASI scoring of single photos by the same or different dermatologist showed mPASI ratings of 50% to 80%, respectively. Repeated mPASI comparison using similarity clustering showed consistent mPASI ratings > 95%. Pearson correlation between absolute scoring and pairwise scoring progression was 0.72.
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