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

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...

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Vydáno v:2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) s. 8 - 15
Hlavní autoři: Rind, Alexander, Slijepcevic, Djordje, Zeppelzauer, Matthias, Unglaube, Fabian, Kranzl, Andreas, Horsak, Brian
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2022
Témata:
Applied computing-Life and medical sciences
Closed box
Computing methodologies-Machine learning
Conferences
Human-centered computing-Visualization-Visualization application domains-Visual analytics
Machine learning
Machine learning algorithms
Time series analysis
Training data
Visual analytics
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Shrnutí: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.
DOI:10.1109/TREX57753.2022.00006