缺血性中风图像中的人工智能:当前的应用和未来的方向
Ying Liu1,2, Zhongjian Wen1,3, Yiren Wang1,3
1School of Nursing, Southwest Medical University, Luzhou, China.
Frontiers in neurology
|July 25, 2024
概括
人工智能 (AI) 对缺血性中风成像有很大的前景,改善了诊断和预测. 克服数据,可解释性和实时更新方面的挑战是临床使用的关键.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 缺血性中风成像分析对于及时诊断和治疗至关重要.
- 当前的诊断方法可能耗时,需要专家解释.
- 人工智能 (AI) 为增强中风成像分析提供了潜在的解决方案.
研究的目的:
- 审查AI在缺血性中风成像中的应用.
- 分析当前的挑战,探索未来的研究方向.
- 突出机器学习 (ML) 和深度学习 (DL) 在中风管理中的潜力.
主要方法:
- 审查当前关于AI在缺血性中风成像中的研究.
- 对人工智能应用的分析,包括心脏病细分,大血管封闭检测,结果预测和附带循环评估.
- 讨论数据局限性和模型可解释性等挑战.
主要成果:
- 人工智能,特别是ML和DL,在提高诊断准确度和预测中风进展方面显示出巨大的潜力.
- 人工智能应用正在开发用于心脏病细分,大血管封闭检测,结果预测和附带循环分级.
- 挑战包括数据量限制,模型可解释性和实时监控的需求.
结论:
- 人工智能在通过先进的成像分析来管理缺血性中风方面具有相当大的价值.
- 需要进一步的研究,以应对技术和实践方面的挑战,以实现广泛的临床采用.
- 开发大型公共数据库和专注于算法解释性对于未来的进步至关重要.
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