论著摘要 |【AI-CT】深度学习指导的中风管理:临床应用的综述(双语版)

2018-01-04 19:33:09 admin 3

Deep learning guided stroke management: a review of clinical applications.

发表日期: 2017.09.27   来源:J Neurointerv Surg. 2017 Sep 27. pii: neurintsurg-2017-013355.

作者:

Rui Feng1, Marcus Badgeley2, J Mocco1, Eric K Oermann1.

作者介绍:

1. Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA.

2. Icahn School of Medicine at Mount Sinai, New York, USA.

摘要

中风是长期残疾的主要原因,结果与及时干预直接相关。然而,不是所有的患者都受益于快速干预。因此,已经大量关注使用神经成像来鉴定尚未经历细胞死亡的缺血区域的潜在益处。灌注-扩散不匹配,被用作一个简单的衡量标准潜在的好处和及时的干预,但半暗带模式提供了一个不准确的预测临床结果。使用深层神经网络(DNN)的深度学习(人工智能)技术的机器学习研究擅长处理复杂的输入。深度学习可能会迫切应用于脑卒中管理的关键领域是图像分割,自动化功能(radiomics)和多模态预测。卷积神经网络的应用,设计用于图像处理的DNN体系结构的系列中心成像数据是成熟的深度学习技术和自然适合从深度学习优势中获益的数据类型之间的完美匹配。这些强大的工具为急性干预和指导预后的数据驱动中风管理开辟了令人兴奋的机会。深度学习技术对于他们可以提供的结果的速度和力量是有用的,并且将成为现代中风专家的武器库中越来越标准的工具,用于向缺血性卒中患者提供个性化药物。

Abstact

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion-diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning's strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist's arsenal for delivering personalized medicine to patients with ischemic stroke.

关键词:

CT灌注,介入,中风,技术,血栓切除术

Keywords:

ct perfusion; intervention; stroke; technology; thrombectomy

阅读原文:PMID: 28954825   DOI: 10.1136/neurintsurg-2017-013355



标签:   人工智能 AI CT 中风 血栓切除术 深度学习

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