基于人工智能的冠状动脉评分在非心电图胸部CT上的血管特异可靠性:与心电图心脏CT进行比较研究
Jing Zhang1, Kefu Liu1, Chenyang You2
1Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
Clinical imaging
|August 5, 2025
概括
人工智能 (AI) 冠状动脉评分 (CACS) 在非心电图 (ECG) 胸部CT上显示出机会性查的前景. 然而,显著的风险错误分类率需要协议优化以提高准确性,特别是对于特定的冠状动脉.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 冠状动脉评分 (CACS) 对心血管风险评估至关重要.
- 电心电图 (ECG) 导入的心脏CT是准确CACS的标准.
- 非心电图胸部CT提供了机会性CACS评估的潜力.
研究的目的:
- 评估基于人工智能的CACS在非心电图胸部CT上的性能.
- 将AI CACS与手动量化 (参考标准) 进行比较.
- 评估每艘船的可靠性和对临床风险分类的影响.
主要方法:
- 对290名患者进行了回顾性分析,对照的是非心电图胸部CT和心电图心脏CT.
- 基于AI的CACS和手动CACS (CACS_man) 的比较,使用类内相关系数 (ICC) 和科恩的kappa.
- 根据心血管计算机断层学会 (SCCT) 的指导方针评估错误类型和解剖分布.
主要成果:
- 胸部CT上的AI CACS和CACS_man (ICC = 0.87) 之间的总体一致性很好.
- 非心电图胸部CT显示了7.5倍更高的风险错误分类率 (41.4%) 与心电图CT (5.5%) 相比.
- 血管特异性分析显示可变可靠性,右冠状动脉 (RCA) 的性能低于最佳,左前下降动脉 (LAD) 的支架错误分类问题.
结论:
- 在非心电图胸部CT上基于AI的CACS对于机会性查很有用.
- 需要对协议进行优化,以解决船舶特定的限制,并减少错误分类率.
- 缓解实质性风险错误分类 (41.4%) 对临床效用至关重要.
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