Episode 128: Machine Learning Algorithms to Predict Seizure Control in Epilepsy Surgery

Episode 128 of the NeurologyLive^® Mind Moments^® podcast is now live! Scroll down to listen or click here to subscribe on your favorite streaming service.

The Mind Moments podcast features exclusive interviews with leaders in the field discussing the latest research and disease management strategies across the breadth of neurology, including epilepsy, multiple sclerosis, Parkinson disease, dementia, sleep disorders, and more.

In this episode, "Machine Learning Algorithms to Predict Seizure Control in Epilepsy Surgery" Lara Jehi, MD, MHCDS, an epilepsy specialist and Cleveland Clinic’s Chief Research and Information Officer, sat down to discuss a recently published study that explored using machine learning algorithms to predict seizure control after epilepsy surgery. In the interview, Jehi explained the unique aspects of the study design, emphasizing the importance of a large, well-characterized patient cohort with consistent follow-up and the choice of scalp EEG—a commonly used, non-invasive test in epilepsy care—as the data source. In addition, Jehi touched on the use of AutoML to streamline the process, enabling efficient identification of the top-performing algorithms and enhancing the model’s predictive accuracy. Furthermore, she spoke on the team needed to properly implement machine learning techniques for neurosurgery, while providing recommendations for other institutions interested in pursuing these types of approaches.

The stories featured in this week's Neurology News Minute, which will give you quick updates on the following developments in neurology, are further detailed here:

• FDA Accepts Resubmitted NDA for Ataluren in Nonsense Duchenne Muscular Dystrophy
• FDA Places Clinical Hold on Epilepsy Agent RAP-219 for Diabetic Peripheral Neuropathic Pain
• First-Ever CRISPR/Cas13-RNA Editing Therapy to be Tested in Phase 1 Study of Age-Related Macular Degeneration

EPISODE BREAKDOWN

• 1:00 – Background on various machine learning approaches for epilepsy research
• 3:20 – Study details, findings, and notable takeaways
• 8:20 – Neurology News Minute
• 10:20 – Novelty in using scalp EEG and its global application
• 15:30 – Team personnel needed for proper implementation of machine learning techniques in epilepsy surgery

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REFERENCE
1. Sheikh SR, McKee ZA, Ghosn S, et al. Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal. Nature. 2024(14):21771. doi:10.1038/s41598-024-72249-7