Residual Seizure Rate of Intermittent Inpatient EEG Compared to a Continuous EEG Model

Diagnosis and management of status epilepticus: improving the status quo

Status epilepticus is a common neurological emergency that is characterised by prolonged or recurrent seizures without recovery between episodes and associated with substantial morbidity and mortality. Prompt recognition and targeted therapy can reduce the risk of complications and death associated with status epilepticus, thereby improving outcomes. The most recent International League Against Epilepsy definition considers two important timepoints in status epilepticus: first, when the seizure does not self-terminate; and second, when the seizure can have long-term consequences, including neuronal injury. Recent advances in our understanding of the pathophysiology of status epilepticus indicate that changes in neurotransmission as status epilepticus progresses can increase excitatory seizure-facilitating and decrease inhibitory seizure-terminating mechanisms at a cellular level. Effective clinical management requires rapid initiation of supportive measures, assessment of the cause of the seizure, and first-line treatment with benzodiazepines. If status epilepticus continues, management should entail second-line and third-line treatment agents, supportive EEG monitoring, and admission to an intensive care unit. Future research to study early seizure detection, rescue protocols and medications, rapid treatment escalation, and integration of fundamental scientific and clinical evidence into clinical practice could shorten seizure duration and reduce associated complications. Furthermore, improved recognition, education, and treatment in patients who are at risk might help to prevent status epilepticus, particularly for patients living in low-income and middle-income countries.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.