Deep learning for prediction of mechanism in acute ischemic stroke using brain diffusion magnetic resonance image
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, bu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Korean Neurocritical Care Society
2023-12-01
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| Series: | Journal of Neurocritical Care |
| Subjects: | |
| Online Access: | http://e-jnc.org/upload/pdf/jnc-230039.pdf |
| Summary: | 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. |
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| ISSN: | 2005-0348 2508-1349 |