Novel Screening Tool for Stroke Using Artificial Neural Network
| Autor: | Ramin Zand, Niyousha Hosseinichimeh, Nitin Goyal, David S Liebeskind, Lucas Elijovich, Jeffrey E. Metter, Raquel Hontecillas, Vida Abedi, Anne W. Alexandrov, Josep Bassaganya-Riera, Georgios Tsivgoulis, Andrei V. Alexandrov |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Male medicine.medical_specialty education Tertiary care Sensitivity and Specificity Brain Ischemia 03 medical and health sciences 0302 clinical medicine medicine Humans Screening tool Stroke Aged Advanced and Specialized Nursing Artificial neural network business.industry Supervised learning Emergency department Middle Aged medicine.disease Backpropagation Confidence interval Surgery 030104 developmental biology Emergency medicine Female Neurology (clinical) Neural Networks Computer Cardiology and Cardiovascular Medicine business 030217 neurology & neurosurgery |
| Zdroj: | Stroke. 48(6) |
| ISSN: | 1524-4628 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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