论文标题
临床步态分析中可信赖的视觉分析：针对脑瘫患者的案例研究
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
论文作者
论文摘要
三维临床步态分析对于选择脑瘫患者（CP）的最佳治疗干预措施至关重要，但会产生大量的时间序列数据。对于对这些数据的自动分析，机器学习方法产生了有希望的结果。但是，由于其黑盒性质，这种方法常常受到临床医生的不信任。我们提出了GaitXplorer，这是一种视觉分析方法，用于将与CP相关的步态模式分类，该方法集成了Grad-CAM（一种可解释的人工智能算法），用于解释机器学习分类。交互式视觉界面中突出显示了高相关区域的分类区域。在两名临床步态专家的案例研究中评估了该方法。他们检查了使用视觉界面的八名患者样本的解释，并表达了他们认为值得信赖的相关性得分，并且发现这些相关性得分可疑。总体而言，临床医生对该方法给出了积极的反馈，因为它们可以更好地了解数据中哪些区域与分类有关。
Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications. Regions of high relevance for classification are highlighted in the interactive visual interface. The approach is evaluated in a case study with two clinical gait experts. They inspected the explanations for a sample of eight patients using the visual interface and expressed which relevance scores they found trustworthy and which they found suspicious. Overall, the clinicians gave positive feedback on the approach as it allowed them a better understanding of which regions in the data were relevant for the classification.
