Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
This addresses the trust issue for clinicians in automated gait analysis for cerebral palsy patients, but it is incremental as it applies existing explainable AI methods to a specific domain.
The authors tackled the problem of clinicians mistrusting black-box machine learning in clinical gait analysis by proposing gaitXplorer, a visual analytics tool that integrates Grad-CAM for explainable AI, which was evaluated in a case study with two experts who gave positive feedback on understanding classification relevance.
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.