Salivary gland ultrasonography (SGUS) has shown good potential in the diagnosis of primary Sjögren's syndrome (pSS). However, a series of international studies have reported needs for improvements of the existing pSS scoring procedures in terms of inter/intra observer reliability before being established as standardized diagnostic tools. The present study aims to solve this problem by employing radiomics features and artificial intelligence (AI) algorithms to make the pSS scoring more objective and faster compared to human expert scoring. The assessment of AI algorithms was performed on a two-centric cohort, which included 600 SGUS images (150 patients) annotated using the original SGUS scoring system proposed in 1992 for pSS. For each image, we extracted 907 histogram-based and descriptive statistics features from segmented salivary glands. Optimal feature subsets were found using the genetic algorithm based wrapper approach. Among the considered algorithms (seven classifiers and five regressors), the best preforming was the multilayer perceptron (MLP) classifier (κ = 0.7). The MLP over-performed average score achieved by the clinicians (κ = 0.67) by the considerable margin, whereas its reliability was on the level of human intra-observer variability (κ = 0.71). The presented findings indicate that the continuously increasing HarmonicSS cohort will enable further advancements in AI-based pSS scoring methods by SGUS. In turn, this may establish SGUS as an effective noninvasive pSS diagnostic tool, with the final goal to supplement current diagnostic tests.

中文翻译：

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基于放射学的唾液腺超声检查对原发性干燥综合征的评估

涎腺超声检查（SGUS）在原发性干燥综合征（pSS）的诊断中显示出良好的潜力。然而，一系列国际研究报告说，在被确立为标准化诊断工具之前，需要在观察者之间/观察者的可靠性方面改进现有的pSS评分程序。本研究旨在通过采用放射学特征和人工智能（AI）算法来解决此问题，与人类专家评分相比，pSS评分更客观，更快捷。AI算法的评估是在两个中心队列中进行的，其中包括600幅SGUS图像（150例患者），这些图像使用了1992年针对pSS提出的原始SGUS评分系统进行了注释。对于每张图像，我们从分割的唾液腺中提取了907个基于直方图的描述性统计特征。使用基于遗传算法的包装器方法找到了最佳特征子集。在考虑的算法（七个分类器和五个回归器）中，最好的预成型是多层感知器（MLP）分类器（κ= 0.7）。MLP的表现远胜于临床医生（κ= 0.67）的平均得分，而其可靠性在人类观察者内部变异性水平（κ= 0.71）上。提出的发现表明，不断增加的HarmonicSS队列将使SGUS在基于AI的pSS评分方法上取得进一步的进步。反过来，这可能会将SGUS确立为有效的非侵入性pSS诊断工具，其最终目标是补充当前的诊断测试。在考虑的算法（七个分类器和五个回归器）中，最好的预成型是多层感知器（MLP）分类器（κ= 0.7）。MLP的表现远胜于临床医生（κ= 0.67）的平均得分，而其可靠性在人类观察者内部变异性水平（κ= 0.71）上。提出的发现表明，不断增加的HarmonicSS队列将使SGUS在基于AI的pSS评分方法上取得进一步的进步。反过来，这可能会将SGUS确立为有效的非侵入性pSS诊断工具，其最终目标是补充当前的诊断测试。在考虑的算法（七个分类器和五个回归器）中，最好的预成型是多层感知器（MLP）分类器（κ= 0.7）。MLP的表现远胜于临床医生（κ= 0.67）的平均得分，而其可靠性在人类观察者内部变异性水平（κ= 0.71）上。提出的发现表明，不断增加的HarmonicSS队列将使SGUS在基于AI的pSS评分方法上取得进一步的进步。反过来，这可能会将SGUS确立为有效的非侵入性pSS诊断工具，其最终目标是补充当前的诊断测试。而其可靠性处于人类观察者内部变异性的水平（κ= 0.71）。提出的发现表明，不断增加的HarmonicSS队列将使SGUS在基于AI的pSS评分方法上取得进一步的进步。反过来，这可能会将SGUS确立为有效的非侵入性pSS诊断工具，其最终目标是补充当前的诊断测试。而其可靠性处于人类观察者内部变异性的水平（κ= 0.71）。提出的发现表明，不断增加的HarmonicSS队列将使SGUS在基于AI的pSS评分方法上取得进一步的进步。反过来，这可能会将SGUS确立为有效的非侵入性pSS诊断工具，其最终目标是补充当前的诊断测试。