Novel Screening Tool for Stroke Using Artificial Neural Network

Vida Abedi; Nitin Goyal; Georgios Tsivgoulis; Niyousha Hosseinichimeh; Raquel Hontecillas; Josep Bassaganya-Riera; Lucas Elijovich; Jeffrey E Metter; Anne W Alexandrov; David S Liebeskind; Andrei V Alexandrov; Ramin Zand

doi:10.1161/STROKEAHA.117.017033

Novel Screening Tool for Stroke Using Artificial Neural Network

Stroke. 2017 Jun;48(6):1678-1681. doi: 10.1161/STROKEAHA.117.017033. Epub 2017 Apr 24.

Affiliations

¹ From the Biocomplexity Institute (V.A., R.Z.), Department of Industrial and Systems Engineering (G.T.), and Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute (R.H., J.B.-R.), Virginia Tech, Blacksburg; Biomedical and Translational Informatics Institute (V.A.) and Department of Neurology (R.Z.), Geisinger Health System, Danville, PA; Department of Neurology, University of Tennessee Health Science Center, Memphis (N.G., G.T., L.E., J.E.M., A.W.A., A.V.A., R.Z.); Second Department of Neurology, "Attikon University Hospital," School of Medicine, University of Athens, Greece (N.H.); and Neurovascular Imaging Research Core and UCLA Stroke Center, University of California, Los Angeles (D.S.L.).
² From the Biocomplexity Institute (V.A., R.Z.), Department of Industrial and Systems Engineering (G.T.), and Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute (R.H., J.B.-R.), Virginia Tech, Blacksburg; Biomedical and Translational Informatics Institute (V.A.) and Department of Neurology (R.Z.), Geisinger Health System, Danville, PA; Department of Neurology, University of Tennessee Health Science Center, Memphis (N.G., G.T., L.E., J.E.M., A.W.A., A.V.A., R.Z.); Second Department of Neurology, "Attikon University Hospital," School of Medicine, University of Athens, Greece (N.H.); and Neurovascular Imaging Research Core and UCLA Stroke Center, University of California, Los Angeles (D.S.L.). ramin.zand@gmail.com.

PMID: 28438906
DOI: 10.1161/STROKEAHA.117.017033

Abstract

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.

Keywords: acute stroke; diagnosis; neural network model.

Publication types

Randomized Controlled Trial
Validation Study

MeSH terms

Aged
Brain Ischemia / diagnosis*
Female
Humans
Male
Middle Aged
Neural Networks, Computer*
Sensitivity and Specificity
Stroke / diagnosis*