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Deep learning for prediction of mechanism in acute ischemic stroke using brain diffusion magnetic resonance image

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Author(s)
Baik-Kyun KimSeung ParkMoon-Ku HanJeong-Ho HongDae-In LeeKyu Sun Yum
Keimyung Author(s)
Hong, Jeong Ho
Department
Dept. of Neurology (신경과학)
Journal Title
J Neurocrit Care
Issued Date
2023
Volume
16
Issue
2
Keyword
Deep learningIschemic strokeEtiologyDiffusion magnetic resonance imaging
Abstract
Background:
Acute ischemic stroke is a disease with multiple etiologies. Therefore, identifying the mechanism of acute ischemic stroke is fundamental to its treatment and secondary prevention. The Trial of Org 10172 in Acute Stroke Treatment classification is currently the most widely used system, but it often has a limitations of classifying unknown causes and inadequate inter-rater reliability. Therefore, we attempted to develop a three-dimensional (3D)-convolutional neural network (CNN)-based algorithm for stroke lesion segmentation and subtype classification using only the diffusion and apparent diffusion coefficient information of patients with acute ischemic stroke.

Methods:
This study included 2,251 patients with acute ischemic stroke who visited our hospital between February 2013 and July 2019.

Results:
The segmentation model for lesion segmentation in the training set achieved a Dice score of 0.843±0.009. The subtype classification model achieved an average accuracy of 81.9%, with accuracies of 81.6% for large artery atherosclerosis, 86.8% for cardioembolism, 72.9% for small vessel occlusion, and 86.3% for control.

Conclusion:
We developed a model to predict the mechanism of cerebral infarction using diffusion magnetic resonance imaging, which has great potential for identifying diffusion lesion segmentation and stroke subtype classification. As deep learning systems are gradually developing, they are becoming useful in clinical practice and applications.
Keimyung Author(s)(Kor)
홍정호
Publisher
School of Medicine (의과대학)
Type
Article
ISSN
2508-1349
Source
https://www.e-jnc.org/journal/view.php?number=404
DOI
10.18700/jnc.230039
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/45334
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Neurology (신경과학)
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