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Novel Screening Tool for Stroke Using Artificial Neural Network.

Vida Abedi¹, Nitin Goyal¹, Georgios Tsivgoulis¹

¹From 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.).

|April 26, 2017

Summary

This summary is machine-generated.

This study developed an artificial neural network (ANN) to accurately diagnose acute cerebral ischemia (ACI) and distinguish it from stroke mimics. The ANN model demonstrated strong performance, aiding in rapid emergency stroke diagnosis.

Keywords:

acute stroke diagnosis neural network model

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Area of Science:

Medical Artificial Intelligence
Neurology
Emergency Medicine

Background:

Timely stroke diagnosis is critical due to high morbidity and narrow treatment windows.
Differentiating acute cerebral ischemia (ACI) from stroke mimics is challenging in emergency settings.

Purpose of the Study:

To develop a supervised learning method for recognizing ACI.
To differentiate ACI from stroke mimics during the initial emergency examination.

Main Methods:

An artificial neural network (ANN) model was developed using backpropagation.
Data from 260 patients with stroke-like symptoms were used for development and validation.
A 10-fold cross-validation method was employed for model validation.

Main Results:

The ANN model achieved an average sensitivity of 80.0% and specificity of 86.2% for ACI diagnosis.
The median precision of the ANN model for ACI diagnosis was 92%.
The model demonstrated robust diagnostic capabilities in a cohort of 260 patients.

Conclusions:

Artificial neural networks (ANNs) are effective tools for ACI recognition.
ANNs can reliably differentiate ACI from stroke mimics in emergency settings.
This technology supports rapid and accurate initial stroke diagnosis.