JMIR Publications

ABSTRACT

Background:

Intraoperative neurophysiological monitoring (IONM) guides the surgeon in ensuring motor pathway integrity during high-risk neurosurgical and orthopedic procedures. Although motor evoked potentials (MEPs) are valuable for predicting motor outcomes, the key features of predictive signals are not well understood, and standardized warning criteria are lacking.

Objective:

The objective of this study is to expand machine learning (ML) methods for muscle classification and test them in a bicentric setup. Further, we aim to identify key features of MEP signals that contribute to accurate muscle classification using explainable artificial intelligence (XAI) techniques.

Methods:

This study employed ML and deep learning models, specifically random forest (RF) classifiers and convolutional neural networks (CNNs), to classify MEP signals from two medical centers according to muscle identity. Depending on the algorithm, time-series, feature-engineered, and time-frequency representations of the MEP data were used. XAI techniques, specifically SHAP values and gradient class activation maps (Grad-CAM), were implemented to identify important signal features.

Results:

High classification accuracy was achieved with the RF classifier, reaching 87.9% accuracy on the validation set and 80.0% accuracy on the test set. The 1D- and 2D-CNNs demonstrated comparably strong performance. Our XAI findings indicate that frequency components and peak latencies are crucial for accurate MEP classification, providing insights that could inform intraoperative warning criteria.

Conclusions:

This study demonstrates the effectiveness of ML techniques and the importance of XAI in enhancing trust in and reliability of AI-driven IONM applications. Further it may help to identify new intrinsic features of MEP signals so far overlooked in conventional warning criteria.