Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier.

Hussam Kaka, Euan Zhang, Nazir Khan
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引用次数: 25

Abstract

There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.

神经放射学中的人工智能和深度学习:探索新前沿。
最近发表了许多关于机器学习(ML)和深度学习在神经放射学中的应用的研究。这些技术性能的提高导致了神经放射学家可获得的商业工具数量不断增加。在这个叙述性的回顾,最近的出版物探索ML在神经放射学评估与几个关键的临床领域的重点。特别回顾了以下方面的主要进展:(1)颅内出血检测,(2)卒中成像,(3)颅内动脉瘤筛查,(4)多发性硬化症成像,(5)神经肿瘤学,(6)头部和肿瘤成像,(7)脊柱成像。
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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