通过基于X射线传感器的冠状动脉血管学来诊断心血管疾病的人工智能技术:一个文献计和系统的审查
Hao Ren1,2, Fengshi Jing1, Yan Fang1
1Faculty of Data Science, City University of Macau, Macao SAR, China.
Digital health
|January 28, 2026
概括
人工智能 (AI) 显著改善了使用冠状动脉血管学进行心血管疾病 (CVD) 诊断. 未来的研究应该专注于大规模验证和可解释的AI用于临床集成.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 基于X射线传感器的冠状动脉造影对于心血管疾病 (CVD) 诊断至关重要.
- 人工智能 (AI) 技术越来越多地用于提高诊断准确性和效率.
研究的目的:
- 系统地评估AI应用在X射线冠状动脉血管学中用于心血管疾病诊断.
- 绘制出版趋势的地图,确定地理和专题热点,并审查AI方法和性能.
- 为未来的研究和AI在心脏病学中的临床整合提供信息.
主要方法:
- 从Web of Science,Scopus和PubMed. 的123个英语出版物 (2010-2025) 的图书统计分析.
- 使用CiteSpace.Space分析出版趋势,国家贡献,共同作者网络和关键词集群.
- 对特定心血管疾病的AI方法,模型架构,数据源和诊断性能指标进行结构化的文献审查.
主要成果:
- 出版趋势显示,2018-2021年间的快速增长,在2022年达到顶峰,美国和中国领先.
- 卷积神经网络 (CNN) 和混合模型占主导地位,在各种CVD中实现了高AUC (0.724-0.997).
- 挑战包括数据集异质性,有限的多中心验证和模型可解释性.
结论:
- 人工智能,特别是深度学习,通过冠状动脉血管学提高了心血管疾病诊断的准确性和效率.
- 目前的证据受到小型数据集,缺乏外部验证和不一致的报告的限制.
- 未来的努力必须优先考虑大规模的多中心验证,可解释的AI和无的临床工作流集成.
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