Title (deu)
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
Description (eng)
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.
Keywords (eng)
Machine learning algorithmsVisual analyticsTraining dataTime series analysisClosed boxMachine learning
Type (eng)
Type (eng)
Language
[eng]
Persistent identifier
Is in series
Title
2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX)
Issued
2022
Publication
IEEE , 2022
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Rights statement (eng)
©2022 IEEE. This is the author’s version of the article that has been published in the proceedings of the 2022 IEEE Workshop onTRust and EXpertise in Visual Analytics (TREX). The final version of this record is available at: 10.1109/TREX57753.2022.00006
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DOI
https://phaidra.ustp.at/o:5182
https://doi.org/10.1109/TREX57753.2022.00006 - Other links
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- RightsLicenseRights statement©2022 IEEE. This is the author’s version of the article that has been published in the proceedings of the 2022 IEEE Workshop onTRust and EXpertise in Visual Analytics (TREX). The final version of this record is available at: 10.1109/TREX57753.2022.00006
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