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利用人工智能进行缺血性脑卒中成像。

Leveraging artificial intelligence in ischemic stroke imaging.

机构信息

The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.

Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.

出版信息

J Neuroradiol. 2022 Jun;49(4):343-351. doi: 10.1016/j.neurad.2021.05.001. Epub 2021 May 11.

Abstract

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.

摘要

人工智能(AI)正在对临床医学产生颠覆性和变革性的影响。及时的临床诊断和影像学检查对于最大限度地降低与缺血性中风相关的发病率和死亡率至关重要。临床医生必须了解人工智能的当前优势和局限性,以提供最佳的患者护理。缺血性中风是人工智能广泛评估的医学领域之一。本文综述了人工智能在中风临床管理中的应用,面向临床医生。在这篇综述中,我们解释了人工智能和机器学习的基本概念。本文没有涉及编码和数学细节,针对的是参与中风管理但没有计算机或数学背景的临床医生。在这里,总结了人工智能在缺血性中风中的应用,并将其分为中风影像(脑梗死的自动诊断、自动 ASPECT 评分计算、梗死分割)、预后预测和治疗患者选择。

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