Department of Dermatology, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
J Eur Acad Dermatol Venereol. 2022 Jan;36(1):68-75. doi: 10.1111/jdv.17711. Epub 2021 Oct 18.
The Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring.
To assess the performance of image-based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image-based scoring by physicians.
Imaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamation, induration and area) in each anatomical region (trunk, arms and legs). The head region was excluded for anonymity. Additionally, PASI-trained physicians retrospectively determined image-based subscores on the test set images of the trunk. Agreement with the real-life scores was determined with the intraclass correlation coefficient (ICC) and compared between the CNNs and physicians.
Intraclass correlation coefficients between the CNN and real-life scores of the trunk region were 0.616, 0.580, 0.580 and 0.793 for erythema, desquamation, induration and area, respectively, with similar results for the arms and legs region. PASI-trained physicians (N = 5) were in moderate-good agreement (ICCs 0.706-0.793) with each other for image-based PASI scoring of the trunk region. ICCs between the CNN and real-life scores were slightly higher for erythema (0.616 vs. 0.558), induration (0.580 vs. 0.573) and area scoring (0.793 vs. 0.694) than image-based scoring by physicians. Physicians slightly outperformed the CNN on desquamation scoring (0.580 vs. 0.589).
Convolutional Neural Networks have the potential to automatically and objectively perform image-based PASI scoring at an anatomical region level. For erythema, desquamation and induration scoring, CNNs performed similar to physicians, while for area scoring CNNs outperformed physicians on image-based PASI scoring.
银屑病面积和严重程度指数（PASI）评分常用于临床实践和研究中，以监测疾病严重程度并确定治疗效果。使用深度学习算法（如卷积神经网络（CNN））对 PASI 评分进行自动化处理，可以实现客观、高效的 PASI 评分。
评估基于图像的 CNN 对 PASI 评分的在解剖区域的性能，并比较 CNN 与医生基于图像的 PASI 评分的性能。
将影像学系列与治疗医生在实际生活中确定的 PASI 亚评分相匹配。使用 576 个躯干、614 个手臂和 541 个腿部区域的标准化成像系列对 CNN 进行训练。为每个解剖区域（躯干、手臂和腿部）的每个 PASI 亚评分（红斑、脱屑、硬结和面积）分别训练 CNN。头部区域因匿名而被排除在外。此外，经过 PASI 训练的医生在躯干的测试集图像上回顾性地确定基于图像的亚评分。用组内相关系数（ICC）确定与真实评分的一致性，并比较 CNN 和医生之间的一致性。
躯干区域的 CNN 与真实评分之间的组内相关系数分别为 0.616、0.580、0.580 和 0.793，用于红斑、脱屑、硬结和面积，手臂和腿部区域也有类似的结果。经过 PASI 训练的 5 名医生（N=5）在基于图像的躯干区域 PASI 评分方面相互之间具有中度至良好的一致性（ICC 为 0.706-0.793）。与医生基于图像的 PASI 评分相比，红斑（0.616 与 0.558）、硬结（0.580 与 0.573）和面积评分（0.793 与 0.694）的 ICC 略高。在脱屑评分方面，医生略优于 CNN（0.580 与 0.589）。
卷积神经网络有可能在解剖区域水平上自动、客观地进行基于图像的 PASI 评分。在红斑、脱屑和硬结评分方面，CNN 的表现与医生相似，而在面积评分方面，CNN 在基于图像的 PASI 评分方面优于医生。
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