Quantifying the impact of a computer‐aided diagnostic score on the clinical diagnosis of functional seizures

Clinicians with more formal seizure training identified possible FDS more accurately than others, but formal training did not guarantee high diagnostic performance. Two performance-based groups, which included 66% readers, benefitted from the FSLS because they identified when to change their mind on the basis of the FSLS's suggestion. The implementation of machine learning in the diagnosis of FDS should focus on identifying clinical settings where it can effectively enhance clinicians' decision-making.

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Publication Details

Journal
Epilepsia
Published
2026-01-14
DOI
https://doi.org/10.1002/epi.70069
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00

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article

Quantifying the impact of a computer‐aided diagnostic score on the clinical diagnosis of functional seizures

E. Cole Patterson, Samuel W. Terman, Piyush Ostwal, Markus Reuber et al.
Epilepsia
EEG and Brain-Computer Interfaces
article

Quantifying the impact of a computer‐aided diagnostic score on the clinical diagnosis of functional seizures

E. Cole Patterson, Samuel W. Terman, Piyush Ostwal, Markus Reuber, Kenneth Kutschman, Wesley T. Kerr, Laura A. Kirkpatrick, Amir H. Karimi, Alex Israel, Feusner Jamie D., Laura A. Strom, Nicholas J. Beimer, Sarah K. Yaghoubi, Danielle Carns, Adriana Koek, James F. Castellano, Laurence W. Knowles, John M. Stern, Bridget MacDonald, Sung Hyun Seo, Lubomir M. Hadjiiski, Meagan M. Watson, Jena Grauer, Payge A Barnard, Zongqi Xia, Page B. Pennell, Corinne H. Allas, Anto I. Bagić, Di Sun, Lomalan Reddy, Rachna Reddy, Siddhika S. Sreenivasan, William C. Stacey, L. Brian Hickman, Alistair Wardrope, Melissa Berry, Najda Robinson‐Mayer, Katherine N. McFarlane, Yanshan Wang
article en

Abstract

Clinicians with more formal seizure training identified possible FDS more accurately than others, but formal training did not guarantee high diagnostic performance. Two performance-based groups, which included 66% readers, benefitted from the FSLS because they identified when to change their mind on the basis of the FSLS's suggestion. The implementation of machine learning in the diagnosis of FDS should focus on identifying clinical settings where it can effectively enhance clinicians' decision-making.

Epilepsia
Centre for Addiction and Mental Health (CA), Emory University (US), University of California, Los Angeles (US), Oregon Health & Science University (US), University of Pittsburgh (US), University of California, San Francisco (US), Lake Erie College of Osteopathic Medicine (US), University of Toronto (CA), University of Exeter (GB), University of Michigan (US), Royal Hallamshire Hospital (GB), Croydon University Hospital (GB), Los Angeles Medical Center (US), Latrobe Regional Hospital (AU), Sheffield Teaching Hospitals NHS Foundation Trust (GB), St George's Hospital (GB), Bradford Teaching Hospitals NHS Foundation Trust (GB), University of Colorado Denver (US)
W. M. Keck Foundation, American Brain Foundation, Epilepsy Society, National Institutes of Health
Quality Education
Openalex Percentile: Top 89%
EEG and Brain-Computer Interfaces
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