Assessing the performance of artificial intelligence in detecting electrographic status epilepticus when human experts often disagree.

Khalid Alsherbini¹, Michelle Armenta Salas², Suganya Karunakaran², Archit Gupta², Baharan Kamousi², Raymond Woo², Veeresh Kumar N Shivamurthy³, David King-Stephens⁴, Lawrence J Hirsch⁵, John Stern⁶, Ioannis Karakis⁷, Vikram R Rao⁸, Farid G Sadaka⁹, Parshaw J Dorriz^10,11, Josef Parvizi¹²

¹Department of Neurology, Banner Health, University of Arizona, Phoenix, AZ, United States.

²Ceribell Inc., Sunnyvale, CA, United States.

³Department of Neurology, Trinity Health, University of Connecticut, Hartford, CT, United States.

⁴Department of Neurology, University of California-Irvine, Irvine, CA, United States.

⁵Department of Neurology, Yale School of Medicine, New Haven, CT, United States.

⁶Department of Neurology, University of California-Los Angeles, Los Angeles, CA, United States.

⁷Department of Neurology, Emory University, Atlanta, GA, United States.

⁸Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.

⁹Department of Adult Critical Care, Mercy Hospital St. Louis, St. Louis, MO, United States.

¹⁰Department of Neurology, Providence Health, Mission Viejo Medical Center, Mission Viejo, CA, United States.

¹¹Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.

¹²Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States.

PubMed PMC DOI PDF

Abstract

OBJECTIVE: Generating reference standards to train and evaluate the accuracy of artificial intelligence (AI) algorithms poses a significant challenge. Particularly when interpreting complex signals like electroencephalography (EEG), where interrater variability is considerable. We aimed to characterize the impact of interrater variability when evaluating the performance of an AI algorithm for dete…

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