Novel Screening Tool for Stroke Using Artificial Neural Network.
Background and Purpose: The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiat...
| Published in: | Stroke (00392499) Vol. 48; no. 6; pp. 1678 - 1682 |
|---|---|
| Main Authors: | , , , , , , , , , , , |
| Format: | research randomized controlled trial Journal Article |
| Published: |
Lippincott Williams & Wilkins
Jun2017
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123184217&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123184217 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00392499 1FT jtl: Stroke (00392499) issn: 00392499 maglogo: N pubinfo: dt: Jun2017 vid: 48 iid: 6 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 123184217 123184217 NLM28438906 123184217 10.1161/STROKEAHA.117.017033 NLM28438906 123184217 ppf: 1678 ppct: 4 formats: tig: atl: Novel Screening Tool for Stroke Using Artificial Neural Network. aug: au: Abedi, Vida Goyal, Nitin Tsivgoulis, Georgios Hosseinichimeh, Niyousha Hontecillas, Raquel Bassaganya-Riera, Josep Elijovich, Lucas Metter, Jeffrey E. Alexandrov, Anne W. Liebeskind, David S. Alexandrov, Andrei V. Zand, Ramin affil: Biocomplexity Institute, Geisinger Health System, Danville, PA sug: subj: Cerebral Ischemia Diagnosis Neural Networks (Computer) Stroke Diagnosis Middle Age Sensitivity and Specificity Human Aged Male Female Validation Studies Comparative Studies Evaluation Research Multicenter Studies Randomized Controlled Trials Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Background and Purpose: The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting.Methods: Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method.Results: A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8-86.3) and 86.2% (95% confidence interval, 78.7-91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7-95.3).Conclusions: Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination. pubtype: Academic Journal doctype: research randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|