论文标题
牛皮癣的严重性评估与相似性聚类机器学习方法减少了观察和观察间变化
Psoriasis Severity Assessment with a Similarity-Clustering Machine Learning Approach Reduces Intra- and Inter-observation variation
论文作者
论文摘要
牛皮癣是一种复杂的疾病，基因型和表型有许多变化。医学的一般进步使医生和皮肤科医生的评估和治疗都更加复杂。即使我们的所有技术进步，我们仍然主要使用评估工具牛皮癣区域和严重程度指数（PASI）进行1970年代开发的严重性评估。在这项研究中，我们评估了一种涉及数字图像，比较Web应用程序和相似性聚类的方法，该方法是为了改善观察者内和观察者间变化的评估工具而开发的。从移动设备收集患者的图像。捕获了相同病变区域相隔约1周的相同病变区域的图像。五位皮肤科医生通过改进的PASI，绝对评分和相对成对的PASI评分评估了牛皮癣的严重程度，并使用相似性群集进行了使用，并使用网络程序每次显示两个图像进行了进行进行。通过相同或不同的皮肤科医生对单张照片的MPASI评分分别显示出50％至80％的MPASI评分。使用相似性聚类的重复MPASI比较显示一致的MPASI等级> 95％。绝对得分与成对得分进程之间的皮尔逊相关性为0.72。
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.
