응급실에서 뇌졸중 조기 발견에 ANN with backprop 적용

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중요도(임상적파급력, 유용성) :3
회기적신기술사용수준 : 3

 

 

날짜 : 2017-06

저널 : Stroke / 신경과 의학저널
국가 : 미국
대학 또는 연구소 : 미국 테네시, 버지니아 공대, 의공학, 신경과
저자:

 

 

한줄요약 : 응급실에 뇌졸중 증상으로 온 환자들을 4시간 이내에, 진짜 뇌줄중인지 아니면 다른 질환인지를 구분하는데 ANN with backprop을 써서 구분하였다. 민감도 80%, 특이도 86%.

 

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.	Stroke. 2017 Jun;48(6):1678-1681. doi: 10.1161/STROKEAHA.117.017033. Epub 2017 Apr 24. Novel Screening Tool for Stroke Using Artificial Neural Network. Abedi V1, Goyal N1, Tsivgoulis G1, Hosseinichimeh N1, Hontecillas R1, Bassaganya-Riera J1, Elijovich L1, Metter JE1, Alexandrov AW1, Liebeskind DS 1, Alexandrov AV1, Zand R2. Author information: 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.   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. © 2017 American Heart Association, Inc.
	PMID: 28438906 [Indexed for MEDLINE]
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