Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy
It addresses the need for reliable AI explanations in medical diagnostics for Cerebral Palsy detection, but is incremental as it evaluates existing XAI methods on a specific dataset without introducing new techniques.
This paper tested the reliability of Explainable AI (XAI) methods, specifically CAM and Grad-CAM, for early detection of Cerebral Palsy using deep learning on skeletal data, finding that both methods effectively identify key body points and are robust to data perturbations, with Grad-CAM outperforming in velocity stability and CAM in bone stability and robustness metrics.
Early detection of Cerebral Palsy (CP) is crucial for effective intervention and monitoring. This paper tests the reliability and applicability of Explainable AI (XAI) methods using a deep learning method that predicts CP by analyzing skeletal data extracted from video recordings of infant movements. Specifically, we use XAI evaluation metrics -- namely faithfulness and stability -- to quantitatively assess the reliability of Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) in this specific medical application. We utilize a unique dataset of infant movements and apply skeleton data perturbations without distorting the original dynamics of the infant movements. Our CP prediction model utilizes an ensemble approach, so we evaluate the XAI metrics performances for both the overall ensemble and the individual models. Our findings indicate that both XAI methods effectively identify key body points influencing CP predictions and that the explanations are robust against minor data perturbations. Grad-CAM significantly outperforms CAM in the RISv metric, which measures stability in terms of velocity. In contrast, CAM performs better in the RISb metric, which relates to bone stability, and the RRS metric, which assesses internal representation robustness. Individual models within the ensemble show varied results, and neither CAM nor Grad-CAM consistently outperform the other, with the ensemble approach providing a representation of outcomes from its constituent models.