呼吸系统流行病中的人工智能-为X型疾病做好准备? 一个范围审查审查
Jennifer Straub1, Enrique Estrada Lobato2, Diana Paez2
1Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090, Vienna, Austria.
European radiology
|November 21, 2024
概括
国际合作对于开发应对未来呼吸系统流行病的快速反应系统至关重要. 利用先进的医学成像和人工智能 (AI) 可以改善健康紧急情况期间的诊断,预后和患者的结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 流行病的准备情况 流行病的准备情况
背景情况:
- 像SARS,MERS,H1N1和COVID-19这样的呼吸系统流行病突出了医疗成像数据管理的挑战.
- 影像分析和人工智能的技术进步与这些健康危机同时发生.
- 过去的数据收集工作是反应性的,导致延迟和阻碍统一的流行病应对策略.
研究的目的:
- 在流行病早期阶段,识别医学成像数据收集,策划和AI分析方面的缺陷.
- 建议采取必要步骤,以利用成像和人工智能增强未来的流行病准备.
- 分析放射学数据在呼吸系统流行病期间的诊断和预后中的作用.
主要方法:
- 在PubMed/MEDLINE,Scopus和Cochrane评论 (2000-2021) 的系统文献搜索.
- 关键词包括成像方式 (成像,放射学,放射学,CT,X射线) 和流行病原体 (SARS,MERS,H1N1,COVID-19).
- 搜索了世卫组织和疾病预防控制中心的数据库,以找到病例定义.
主要成果:
- 医学成像在呼吸系统流行病的早期阶段起着至关重要的作用.
- 数据收集往往是临时和延迟的,阻碍了统一指南和人工智能工具的开发.
- 需要积极主动,国际协调的流行病数据战略.
结论:
- 国际合作对于建立有效,快速应对未来流行病的系统至关重要.
- 先进的医学成像和人工智能是改善流行病管理的关键组成部分.
- 加强全球合作将提高诊断和预后能力,最终改善患者的治疗结果.
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