Expert Commentary: Evaluating the Reliability of Explainable AI in Predicting Cerebral Palsy
This study explores the potential of Explainable AI (XAI) methods in predicting Cerebral Palsy (CP) by analyzing skeletal data extracted from video recordings of infant movements. Early detection of CP is crucial for effective intervention and monitoring, making this research significant for improving diagnosis and treatment outcomes.
One of the main challenges in using deep learning models for medical applications is the lack of interpretability. XAI aims to address this issue by providing explanations of the model’s decision-making process, enabling medical professionals to understand and trust the predictions.
In this study, the authors employ two XAI methods, namely Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), to detect key body points influencing CP predictions. They utilize a unique dataset of infant movements and apply skeleton data perturbations to evaluate the reliability and applicability of these XAI methods.
The evaluation metrics used in this study are faithfulness and stability. Faithfulness measures the extent to which the XAI method’s explanations align with the model’s actual decision criteria. Stability, on the other hand, evaluates the robustness of the explanations against minor data perturbations.
The results indicate that both CAM and Grad-CAM effectively identify key body points influencing CP predictions. However, the performance differs in terms of specific metrics. Grad-CAM outperforms CAM in terms of stability, particularly in measuring velocity (RISv). This indicates that Grad-CAM’s explanations remain consistent even when there are slight fluctuations in the data. On the other hand, CAM performs better in measuring bone stability (RISb) and internal representation robustness (RRS).
Another interesting finding of this study is the evaluation of the XAI metrics for both the overall ensemble and the individual models within the ensemble. The ensemble approach provides a representation of outcomes from its constituent models, demonstrating the potential for combining multiple models to improve prediction accuracy and interpretability.
It is worth noting that the individual models within the ensemble show varied results, and neither CAM nor Grad-CAM consistently outperform the other. This suggests that the ensemble approach leverages the diversity of the constituent models to provide a more comprehensive understanding of the prediction process.
Overall, this study demonstrates the reliability and applicability of XAI methods, specifically CAM and Grad-CAM, in predicting CP using skeletal data extracted from video recordings of infant movements. The findings contribute to the field of medical AI, showing the potential for XAI to improve the interpretability and trustworthiness of deep learning models in healthcare applications.