Role of Artificial Intelligence in Brain Stroke Management: A survey

Suhavi Kaur Bhatia, S. Goyal, Tripatjot Singh Arora, Rishu Chhabra
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Abstract

Deep Learning (DL) and Machine Learning (ML) are the key subsets of Artificial Intelligence that have evolved into an important tool in healthcare settings. Brain stroke management is one of the applications where these computer based techniques can help the patients with better diagnosis and individualized clinical care. However, stroke diagnosis and prognosis are dependent on a number of clinical and individual factors. To increase diagnostic and prognostic accuracy, the development of efficient ML and DL algorithms and thorough data collection and assimilation is the key. In this paper, we present a survey of deep learning and machine learning techniques for brain stroke management. The techniques have been categorized on the basis of type of cerebral stroke: ischemic stroke and hemorrhagic stroke. The paper concludes with the future research directions in the area of brain stroke management.
人工智能在脑卒中管理中的作用:一项调查
深度学习(DL)和机器学习(ML)是人工智能的关键子集,已发展成为医疗保健环境中的重要工具。脑卒中管理是这些基于计算机的技术可以帮助患者更好地诊断和个性化临床护理的应用之一。然而,中风的诊断和预后取决于许多临床和个人因素。为了提高诊断和预后的准确性,开发高效的ML和DL算法以及彻底的数据收集和同化是关键。在本文中,我们介绍了深度学习和机器学习技术在脑卒中管理中的应用。这些技术根据脑卒中的类型分为:缺血性脑卒中和出血性脑卒中。最后,对今后脑卒中治疗领域的研究方向进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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