Improving Ischemic Stroke Care With MRI and Deep Learning Artificial Intelligence.

Yannan Yu, Jeremy J Heit, Greg Zaharchuk
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引用次数: 7

Abstract

Abstract: Advanced magnetic resonance imaging has been used as selection criteria for both acute ischemic stroke treatment and secondary prevention. The use of artificial intelligence, and in particular, deep learning, to synthesize large amounts of data and to understand better how clinical and imaging data can be leveraged to improve stroke care promises a new era of stroke care. In this article, we review common deep learning model structures for stroke imaging, evaluation metrics for model performance, and studies that investigated deep learning application in acute ischemic stroke care and secondary prevention.

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利用MRI和深度学习人工智能改善缺血性脑卒中护理。
摘要:先进的磁共振成像技术已成为急性缺血性脑卒中治疗和二级预防的选择标准。利用人工智能,特别是深度学习,来综合大量数据,并更好地了解如何利用临床和成像数据来改善中风治疗,有望开创中风治疗的新时代。在本文中,我们回顾了脑卒中成像常用的深度学习模型结构,模型性能的评估指标,以及深度学习在急性缺血性脑卒中护理和二级预防中的应用研究。
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来源期刊
Topics in Magnetic Resonance Imaging
Topics in Magnetic Resonance Imaging Medicine-Medicine (all)
CiteScore
5.50
自引率
0.00%
发文量
24
期刊介绍: Topics in Magnetic Resonance Imaging is a leading information resource for professionals in the MRI community. This publication supplies authoritative, up-to-the-minute coverage of technical advances in this evolving field as well as practical, hands-on guidance from leading experts. Six times a year, TMRI focuses on a single timely topic of interest to radiologists. These topical issues present a variety of perspectives from top radiological authorities to provide an in-depth understanding of how MRI is being used in each area.
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