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紧急神经放射学中的人工智能:当前的应用和前景

Bo Gong¹, Farzad Khalvati², Birgit B Ertl-Wagner³

¹Department of Medical Imaging, University of Toronto, Toronto, Ontario, M5T 1W7, Canada; Department of Computer Science. University of Toronto, Toronto, Ontario, M5S 2E4, Canada.

Diagnostic and interventional imaging

|December 13, 2024

概括

人工智能 (AI) 正在改变紧急神经放射学,改善对中风和出血等疾病的诊断. 需要进一步开发,特别是对于儿科神经成像和现实世界的性能分析.

科学领域:

神经辐射学神经辐射学
人工智能的人工智能
医疗成像医学成像

背景情况:

紧急神经放射学对于大脑,头部,部和脊柱的急性疾病至关重要.
在这个领域的人工智能 (AI) 应用已经迅速发展.
人工智能为改善诊断速度和准确性提供了潜力.

研究的目的:

为应急神经放射学中人工智能应用提供最新的审查.
在中风,出血和其他急性病理中分析机器学习和深度学习.
讨论人工智能在神经放射学中的实际考虑和未来方向.

主要方法:

在紧急神经放射学中对人工智能的当前文献进行叙述性审查.
对各种急性疾病的AI算法 (机器学习,深度学习) 的分析.
包括商业产品示例和对成像模式的讨论.

主要成果:

人工智能显示,急性疾病的深度和范围显著扩大.
详细的分析涵盖了急性缺血性中风,内出血和血管病理中的AI.
还讨论了感染,骨折,带压缩和儿科成像中的应用.

结论:

人工智能正在大大影响紧急神经放射学诊断.
临床需求驱动的开发,儿科神经成像专注和现实世界的绩效分析至关重要.
人工智能有望提高紧急神经放射学工作流程和患者的治疗结果.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.紧急神经辐射学机器学习是机器学习.

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