Real-world evaluation of an automated EEG spike detection software in a tertiary centre compared to a clinical reference standard

This large-scale study of a machine learning model for identification of IEDs in a representative clinical population found a high negative predictive value suggesting that this may be a useful tool to rule out IEDs. However, the low positive predictive value demonstrates the potential for over-calling IEDs in routine EEGs. Future research should evaluate machine learning models alongside clinical feedback before this approach can have sufficient utility in direct clinical care.