验证人工智能软件用于心脏和胸部计算机断层扫描中的自动评分
I Iris Hamelink1, Z Zhenhui Nie2, T E J Thom Severijn3
1Department of Radiology, University of Groningen, University Medical Center of Groningen 9713GZ Groningen, the Netherlands.
European journal of radiology
|July 21, 2025
概括
冠状动脉评分 (CACS) 的人工智能 (AI) 显示出高精度和与手工方法的强烈一致. 这项技术可以提高效率,并使冠状动脉疾病的机会性查成为可能.
科学领域:
- 心血管成像 - 心血管成像
- 放射学中的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 冠状动脉评分 (CACS) 使用阿加斯顿 (AS) 或体积评分 (VS) 量化化.
- 手动CACS可能耗时,需要更高效的方法.
- 自动化的人工智能工具为更快,更一致的CACS分析提供了潜力.
研究的目的:
- 为了比较基于人工智能 (AI) 的自动化CACS与手动评分.
- 为了评估AI在心脏CT和胸部CT中的性能,用于肺癌查.
- 与传统方法相比,评估人工智能驱动的CACS的准确性和一致性.
主要方法:
- 一组684名参与者 (平均年龄59岁) 接受了心脏和非心电图触发的胸部CT.
- 基于AI的CACS结果与手动AS和VS进行了比较.
- 分析包括敏感性,准确性,类内相关系数 (ICC),布兰德-阿尔特曼分析和科恩风险分层的卡帕.
主要成果:
- 人工智能实现了高检测率:98.1%的心脏CT (97.1%准确率) 和92.4%胸部CT (92.1%准确率).
- 在AI和手动评分之间观察到很好的一致性 (在两种扫描类型中,AS和VS的ICC>0.99).
- 科恩的卡帕显示了风险分类的实质性到几乎完美的一致性 (心脏CT为0.952,胸部CT为0.901).
结论:
- 基于AI的CACS提供了冠状动脉的准确和可靠的量化.
- 人工智能工具与手动评分和有效的风险分层表现出强烈一致.
- 人工智能有可能显著减少CACS评估时间,并促进胸部CT扫描的机会性查.
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