Evidence map›Paper›PMID 41533542›Full record

ArticleEpilepsia2026

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

Wesley T Kerr, Katherine N McFarlane, Corinne H Allas, Samuel W Terman, Markus Reuber, Sung Hyun Seo, Payge Barnard, Adriana Y Koek, Amir H Karimi, Siddhika S Sreenivasan and 29 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epilepsia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

39 authors.

Wesley T KerrDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-5546-5951
Katherine N McFarlaneDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0009-0000-0365-813X
Corinne H AllasDepartment of Neurology, University of California, Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-7329-480X
Samuel W TermanDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0001-6179-9467
Markus ReuberAcademic Neurology Unit, University of Sheffield, Royal Hallamshire Hospital, Sheffield, UK.ORCID https://orcid.org/0000-0002-4104-6705
Sung Hyun SeoDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0001-7723-9317
Payge BarnardDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0009-0009-1314-9887
Adriana Y KoekDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-4806-3743
Amir H KarimiDepartment of Neurology, University of California, Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0003-3560-9433
Siddhika S SreenivasanDepartment of Neurology, University of California, Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-3353-5031
Jena S GrauerDepartment of Neurology, University of California, Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0001-8272-5221
Di SunDepartment of Neurology, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0001-6578-5142
Meagan WatsonDepartment of Neurology, University of Colorado, Aurora, Colorado, USA.ORCID https://orcid.org/0000-0002-2977-681X
Elissa H PattersonDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-8892-6435
James CastellanoDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-1052-8725
Anto I BagićDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-6284-8336
L Brian HickmanDepartment of Neurology, University of California, Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-7685-9562
Kenneth H KutschmanDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.
Laurence KnowlesDepartment of Neurology, University of Exeter, Exeter, UK.ORCID https://orcid.org/0000-0003-2316-4676
Alistair WardropeAcademic Neurology Unit, University of Sheffield, Royal Hallamshire Hospital, Sheffield, UK.ORCID https://orcid.org/0000-0003-3614-6346
Piyush OstwalDepartment of Neurology, Latrobe Regional Hospital, Traralgon, Victoria, Australia.ORCID https://orcid.org/0000-0001-6057-2578
Bridget K MacDonaldDepartment of Neurology, Croydon University Hospital, Thornton Health, UK.ORCID https://orcid.org/0000-0002-9435-5157
Melissa BerryDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-4288-7557
Lomalan ReddyCommunity Epilepsy Service, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK.
Najda Robinson-MayerDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.
Nicholas J BeimerDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-8054-5769
Rachna ReddyDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Sarah K YaghoubiDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Laura A KirkpatrickDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-8259-5997
Danielle R CarnsDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0009-0009-9382-8993
Alex J IsraelDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0001-9429-5089
Jamie D FeusnerDepartment of Psychiatry, Division of Neurosciences and Clinical Translation, University of Toronto, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0002-0391-345X
Zongqi XiaDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-1500-2589
Yanshan WangDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-4433-7839
Laura A StromDepartment of Neurology, University of Colorado, Aurora, Colorado, USA.ORCID https://orcid.org/0000-0002-3297-5908
Page B PennellDepartment of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0003-0568-2795
John M SternDepartment of Neurology, University of California, Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-3549-1642
Lubomir HadjiiskiDepartment of Radiology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0003-2069-8066
William C StaceyDepartment of Neurology, University of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-8359-8057

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
Child Neurologist Career Development Program (CNCDP)K12NS098482 · NINDS · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI SCHLAGGAR, BRADLEY L · 2016 to 2025
$26.2M
IN VIVO STUDIES OF THE EPILEPTIC HIPPOCAMPUSR01NS033310 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI JEROME NONE ENGEL, Richard Staba · 1994 to 2026
$7.5M
Translational Neuroscience Training GrantR25NS065723 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CARMICHAEL, STANLEY THOMAS · 2009 to 2023
$2.8M
Comprehensive Training in Neuroimaging Fundamentals and ApplicationsR90DA023422 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.7M
NeuroNEXT Clinical Research Site at the University of MichiganU24NS107158 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHOU, KELVIN L · 2018 to 2022
$1.6M
Comprehensive Training in Neuroimaging Fundamentals and ApplicationsT90DA022768 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.5M
UM Clinical Neuroscientist Training ProgramR25NS089450 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALBIN, ROGER L · 2014 to 2023
$1.3M
Feasibility of machine learning to improve the diagnostic odyssey for functional seizuresK23NS135134 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Wesley Thomas Kim Kerr · 2025 to 2026
$448k
American Academy of NeurologyChristina Louise George TrustNICHD NIH HHS L40 HD113008NIDA NIH HHS R90 DA023422NIDA NIH HHS T90 DA022768NIGMS NIH HHS T32 GM008042NIH HHS K23NS135134NIH HHS P20NS080131NIH HHS R01NS033310NIH HHS R25NS065723NIH HHS R25NS089450NIH HHS R90DA022768NIH HHS R90DA023422NIH HHS T32GM08042NIH HHS T90DA022768NIH HHS U24NS107158NINDS NIH HHS K12 NS098482NINDS NIH HHS K12NS098482NINDS NIH HHS K23 NS135134NINDS NIH HHS R01 NS033310NINDS NIH HHS R25 NS065723NINDS NIH HHS R25 NS089450NINDS NIH HHS U24 NS107158W. M. Keck Foundation
6 · The paper itself

Abstract

objectiveThe diagnosis of functional/dissociative seizures (FDS) without ictal video-electroencephalography is challenging. The Functional/Dissociative Seizures Likelihood Score (FSLS) is a machine learning-based diagnostic score that aims to help clinicians identify FDS. We evaluated whether a human-in-the-loop implementation of the FSLS improved the performance of clinicians identifying FDS as compared to epileptic seizures (ES).

methodsWe constructed 117 anonymized cases about patients with ictal video-electroencephalography-documented FDS, epilepsy, co-occurring ES and FDS, or physiological seizurelike events. Text-based clinical history was presented followed by the FSLS. Readers were asked the most likely diagnosis after each piece of information. We used mixture modeling combined with mixed effects logistic regression to perform data-driven grouping of participants based on observed patterns of diagnostic performance.

resultsOverall, 163 readers saw 1142 cases (median = 4 cases/reader), and 146 (90%) had a performance higher than chance. More formal training in seizures was associated with better performance (epileptologist accuracy = 67%, mental health clinician accuracy = 52%). Data-driven groups including 66% of readers benefitted from the FSLS (accuracy improvement = 12%-15%, p < .05), including those in the reference and near highest baseline performance group. Other groups had no net change in performance (p > .75). SIGNIFICANCE: 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.

Indexed as

Diagnosis, Computer-AssistedEpilepsyMachine LearningSeizuresAdultElectroencephalographyFemaleHumansMaleVideo Recordingartificial intelligenceepilepsymachine learningobserver studypsychogenic nonepileptic seizures (PNES)

Identifiers

PMID41533542
PMCPMC12959490

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.