Quantitative comparison of explainable AI methods for interpreting deep learning-based classification of 3D gait...

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Clinical Biomechanics

Background:

• Gait disorders, including those from cerebral palsy and neuromuscular diseases, require accurate assessment.
• 3D clinical gait analysis (3DGA) is a key tool for evaluating gait abnormalities.
• Previous deep learning (DL) models combined with 3DGA showed high diagnostic accuracy for childhood gait disorders but lacked transparency.

Purpose of the Study:

• To improve the interpretability and trustworthiness of DL-based diagnostic tools for gait disorders.
• To identify the critical features driving DL model diagnoses using Explainable AI (XAI).
• To enhance diagnostic accuracy by focusing on essential gait parameters.

Main Methods:

• Applied four XAI methods (LIME, DeepLift, Integrated Gradients, sequential feature selection) to DL models analyzing 3DGA data.
• Utilized three distinct datasets covering various gait disorders.
• Evaluated the relevance and reliability of highlighted features across different network architectures.

Main Results:

• XAI methods successfully identified relevant and reliable features for gait disorder diagnosis.
• Integrated Gradients was identified as the most suitable XAI method for this application.
• Utilizing a subset of critical features led to improved diagnostic accuracy compared to using all features.

Conclusions:

• This research elucidates the diagnostic basis of DL models in 3DGA through XAI.
• Focusing on critical features identified by XAI enhances diagnostic performance.
• Improved model interpretability fosters greater clinician understanding and trust in AI diagnostic tools for gait analysis.