Welcome to the NeurologyLive® Mind Moments® podcast. Tune in to hear leaders in neurology sound off on topics that impact your clinical practice.

In this episode, 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.

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

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:

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