Artificial Intelligence and Machine Learning in Neuromodulation for Epilepsy

SUMMARY: Recent advances in artificial intelligence (AI) and machine learning (ML) can revolutionize neuromodulation therapies for drug-resistant epilepsy. Successful incorporation of AI/ML methods into the management of epilepsy can guide treatment decisions, enable interventions to adapt to dynamic epileptic networks, and hopefully improve patient outcomes. We introduce some common concepts in ML, focusing on neural networks, particularly convolutional and recurrent neural networks, and support vector machines, because these methods have been commonly applied to epilepsy neuromodulation. We discuss current AI/ML applications in neuromodulation, encompassing vagus nerve stimulation, responsive neurostimulation, and deep brain stimulation, for the treatment of epilepsy. We consider how AI/ML methods leverage large data sets to enhance patient-specific epileptic network analysis, optimize stimulation targets, and refine closed-loop systems for real-time seizure detection and termination. AI/ML applications extend to recognizing autonomic and behavioral seizure surrogates, detecting interictal epileptiform activity, and forecasting seizures for preemptive interventions. Furthermore, AI-powered neuroimaging analysis can enhance segmentation accuracy for precise electrode placement, which can improve neuromodulation outcomes. We review which AI/ML tools have been applied to each problem, as well as their relative performance. Challenges remain, however, in translating AI/ML models into clinical settings due to interpatient variability and limited real-world validation. Future directions include integrating behavioral signals, developing AI-assisted clinical decision tools, and refining energy-efficient neurostimulation designs. Large language models and generative AI hold promise for optimizing patient-specific neuromodulation strategies. However, further research is required to validate AI/ML applications in clinical practice, enhance model generalizability, and address ethical concerns surrounding data privacy and AI-driven decision making.