Evidence map›Paper›PMID 42347801›Full record

ArticleEpilepsia2026

Accuracy of the rapid-response electroencephalography's Automated Seizure Burden Estimator: A follow-up validation study of version 8 (AccuRASE II).

Zubeda B Sheikh, Michael W K Fong, Monica B Dhakar, Wei Fang, Neishay Ayub, Janine Molino, Hiba A Haider, Brandon Foreman, Emily J Gilmore, Moshe Mizrahi and 5 more

Abstract readValidation Study
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.

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0citing papers in PubMed
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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

15 authors.

Zubeda B SheikhDepartment of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0002-0857-7993
Michael W K FongDepartment of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0002-5666-5234
Monica B DhakarDepartment of Neurology, Brown University, Providence, Rhode Island, USA.ORCID https://orcid.org/0009-0007-4518-8827
Wei FangHealth Sciences Center, University of New Mexico, Albuquerque, New Mexico, USA.ORCID https://orcid.org/0000-0002-0683-8846
Neishay AyubDepartment of Neurology, Brown University, Providence, Rhode Island, USA.ORCID https://orcid.org/0000-0001-5490-8776
Janine MolinoDepartment of Orthopaedics, Warren Alpert Medical School at Brown University; Biostatistics, Epidemiology, Research Design, and Informatics Core, Rhode Island Hospital, Providence, Rhode Island, USA.ORCID https://orcid.org/0000-0002-6258-3913
Hiba A HaiderDepartment of Neurology, University of Chicago Medical Center, Chicago, Illinois, USA.ORCID https://orcid.org/0000-0001-7118-3690
Brandon ForemanDepartment of Neurology & Rehabilitation Medicine, University of Cincinnati Medical Center, Cincinnati, Ohio, USA.ORCID https://orcid.org/0000-0002-5418-674X
Emily J GilmoreDepartment of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0002-4659-6861
Moshe MizrahiDepartment of Neurology & Rehabilitation Medicine, University of Cincinnati Medical Center, Cincinnati, Ohio, USA.ORCID https://orcid.org/0000-0002-1332-6478
Ioannis KarakisDepartment of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA.ORCID https://orcid.org/0000-0001-5122-7211
Sarah SchmittDepartment of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA.ORCID https://orcid.org/0000-0003-1384-9794
Gamaleldin OsmanDepartment of Neurology, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0002-2231-2473
Ji Yeoun YooDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID https://orcid.org/0000-0001-7242-9779
Lawrence J HirschDepartment of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0002-6333-832X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveCeribell Inc.'s point-of-care electroencephalographic (EEG) system and artificial intelligence-based Automated Seizure Burden Estimator (ASBE; ClarityPro) have US Food and Drug Administration clearance for diagnosing electrographic status epilepticus (ESE). The AccuRASE study using ASBE version 6 (V6) showed high negative predictive value (NPV) but limited sensitivity and positive predictive value (PPV) at certain thresholds. Version 8 (V8) is the updated algorithm trained with additional EEG samples.

methodsWe tested V8 on the previously used rapid-response EEG test dataset (not used to train V8). Sensitivity, specificity, PPV, and NPV were compared against blinded expert annotations. ESE, ESE and possible ESE (ESE/pESE), electrographic seizures (ESz), and Esz with highly epileptiform patterns (Esz/HEPs) at burden thresholds of >1%, >10%, >20%, >50%, and >90% were analyzed. Additionally, we evaluated the built-in ESE and ESE/pESE alerts (≥90% over 5 min, ≥20% over 1 h, or ≥10 continuous minutes of seizure).

resultsV8 showed 100% sensitivity at lower thresholds (>10%-20%) for ESE (V6 .86), without a specificity loss (V8 .9 vs. V6 .85), and retained NPV (V8 1.0 vs. V6 .99). Sensitivity at 50% threshold was .86 for ESE (V6 .71), and specificity was .94 (V6 .91); PPV for ESE was 30% for ESE and 80% for ESE/pESE. The specificity for ESE remained high for 90% burden (V8 .96, V6 .97) but the sensitivity dropped (.29 vs. V6 .43). Sensitivity was lower for ESz/HEPs (.54 → .44 at >10% threshold), although the algorithm is not designed to detect HEPs. Built-in alert analysis showed significantly improved sensitivity for ESE (.57 → .86); specificity and NPV remained high (>.94). SIGNIFICANCE: ASBE V8 showed meaningful improvements in sensitivity, continued excellent NPV, and improved built-in alert accuracy for ESE and ESE/pESE. The best seizure burden threshold for ruling out ESE now is 20% (vs. 10% with V6) and that for ruling in ESE/pESE with reasonable certainty is 50% (vs. 90% with V6).

Indexed as

ElectroencephalographySeizuresStatus EpilepticusAlgorithmsArtificial IntelligenceFemaleFollow-Up StudiesHumansMalePoint-of-Care SystemsPredictive Value of TestsReproducibility of ResultsSensitivity and Specificityartificial intelligenceautomatedpoint of care (POC) EEGrapid response EEGseizure detection

Identifiers

PMID42347801
PMCPMC13592501

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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.