Evidence map›Paper›PMID 35659609›Full record

ArticleBMC neurology2022

Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts.

Grant C O'Connell, Kyle B Walsh, Christine G Smothers, Suebsarn Ruksakulpiwat, Bethany L Armentrout, Chris Winkelman, Truman J Milling, Steven J Warach, Taura L Barr

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Artificial intelligence for early diagnosis in emergency department.Journal of anesthesia, analgesia and critical care · 2026
    Review
  3. Article
  4. Article
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

9 authors.

Grant C O'ConnellSchool of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4904, USA. grant.oconnell@case.edu.
Kyle B WalshDepartment of Emergency Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
Christine G SmothersSchool of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4904, USA.
Suebsarn RuksakulpiwatSchool of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4904, USA.
Bethany L ArmentroutSchool of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4904, USA.
Chris WinkelmanSchool of Nursing, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4904, USA.
Truman J MillingDell Seton Medical Center, University of Texas, Austin, TX, USA.
Steven J WarachDell Seton Medical Center, University of Texas, Austin, TX, USA.
Taura L BarrSchool of Nursing, Ohio State University, Columbus, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe development of tools that could help emergency department clinicians recognize stroke during triage could reduce treatment delays and improve patient outcomes. Growing evidence suggests that stroke is associated with several changes in circulating cell counts. The aim of this study was to determine whether machine-learning can be used to identify stroke in the emergency department using data available from a routine complete blood count with differential.

methodsRed blood cell, platelet, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were assessed in admission blood samples collected from 160 stroke patients and 116 stroke mimics recruited from three geographically distinct clinical sites, and an ensemble artificial neural network model was developed and tested for its ability to discriminate between groups.

resultsSeveral modest but statistically significant differences were observed in cell counts between stroke patients and stroke mimics. The counts of no single cell population alone were adequate to discriminate between groups with high levels of accuracy; however, combined classification using the neural network model resulted in a dramatic and statistically significant improvement in diagnostic performance according to receiver-operating characteristic analysis. Furthermore, the neural network model displayed superior performance as a triage decision making tool compared to symptom-based tools such as the Cincinnati Prehospital Stroke Scale (CPSS) and the National Institutes of Health Stroke Scale (NIHSS) when assessed using decision curve analysis.

conclusionsOur results suggest that algorithmic analysis of commonly collected hematology data using machine-learning could potentially be used to help emergency department clinicians make better-informed triage decisions in situations where advanced imaging techniques or neurological expertise are not immediately available, or even to electronically flag patients in which stroke should be considered as a diagnosis as part of an automated stroke alert system.

Indexed as

StrokeTriageCell CountEmergency Service, HospitalHumansNeural Networks, ComputerDecision supportDigital healthEmergency medicineImmune systemMachine-learningNLRStrokeStroke scalesTriageWBC

Identifiers

PMID35659609
PMCPMC9164330

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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.