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

TL;DR

This paper introduces gaitXplorer, a visual analytics tool that uses explainable AI to help clinicians understand machine learning classifications of gait patterns in cerebral palsy patients, improving trust and interpretability.

Contribution

The paper presents gaitXplorer, integrating Grad-CAM with visual analytics to enhance interpretability of ML models in clinical gait analysis for CP patients.

Findings

Clinicians found the explanations trustworthy and helpful.

The approach improved understanding of relevant gait regions.

Positive feedback from clinical experts supports its practical utility.

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.