Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy

• URL: http://arxiv.org/abs/2208.05232v1
• Date: Wed, 10 Aug 2022 09:21:28 GMT
• Title: Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
• Authors: Alexander Rind (1), Djordje Slijep\v{c}evi\'c (1), Matthias Zeppelzauer (1), Fabian Unglaube (2), Andreas Kranzl (2) and Brian Horsak (3) ((1) Institute of Creative\Media/Technologies, St. Poelten University of Applied Sciences, Austria, (2) Orthopaedic Hospital Vienna-Speising, Austria, (3) Institute of Health Sciences, St. Poelten University of Applied Sciences, Austria)
• Abstract summary: gaitXplorer is a visual analytics approach for the classification of CP-related gait patterns. It integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications.
• Score: 43.55994393060723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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