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相关概念视频

Stroke: Introduction and Types01:29

Stroke: Introduction and Types

A stroke is an acute neurological event caused by the sudden disruption of cerebral blood flow, leading to rapid loss of neuronal function. Neurons depend on continuous oxygen and glucose supply, so even brief interruptions can cause irreversible injury within minutes. Strokes are classified into ischemic and hemorrhagic types.Ischemic StrokeIschemic strokes are most common and occur due to arterial occlusion, depriving brain tissue of oxygen and nutrients. This leads to energy failure, ionic...

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综合性审查:机器和深度学习在脑中风诊断中的作用

João N D Fernandes1,2,3, Vitor E M Cardoso4,3, Alberto Comesaña-Campos5,6

  • 1INESC TEC, 4200-465 Porto, Portugal.

Sensors (Basel, Switzerland)
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概括

机器学习和深度学习显示出预测脑中风 (脑血管事故) 风险和改善患者护理的前景. 本综述分析了人工智能在中风诊断中的应用,并强调了改善健康监测的未来研究方向.

关键词:
大脑中风 脑卒中 大脑中风这是分类分类的分类.深度学习是一种深度学习.机器学习是机器学习.对象检测检测对象检测对象检测细分化 细分化的细分化

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 神经学 神经学

背景情况:

  • 脑中风 (脑血管事故) 是全球死亡和残疾的主要原因.
  • 精确预测和诊断中风对于有效的管理和患者的结果至关重要.
  • 现有的分析方法与中风风险因素的复杂性作斗争.

研究的目的:

  • 综合审查机器学习 (ML) 和深度学习 (DL) 在脑中风诊断中的应用.
  • 在人工智能驱动的中风分析中确定当前的挑战和未来的研究方向.
  • 为脑中风研究提供相关数据集的精选列表.

主要方法:

  • 在2020-2024年期间发表的25篇评论论文的系统审查,遵守PRISMA指南.
  • 专注于ML/DL应用在中风分类,细分和物体检测方面.
  • 评估用于预测性健康监测的先进传感器系统.

主要成果:

  • ML和DL技术为识别中风预测因子提供先进的数据处理.
  • 这些人工智能方法提高了诊断准确度,并使个性化护理建议成为可能.
  • 该审查综合了人工智能模型性能评估和验证的发现.

结论:

  • 人工智能,特别是ML和DL,具有改变脑中风诊断和患者护理的巨大潜力.
  • 需要进一步的研究来应对当前的挑战,并优化神经学中的AI应用.
  • 与人工智能集成的先进传感器系统可以改善对中风的预测健康监测.