基于AI的自动冠状动脉评分使用SCAPIS的追溯CT数据是准确的,与专家评分相关
Lilian Henriksson1,2, Mårten Sandstedt3,4, Patrik Nowik5,6
1Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. lilian.henriksson@regionostergotland.se.
European radiology
|October 17, 2024
概括
人工智能 (AI) 与冠状动脉评分 (CACS) 的半自动评估表现出了很好的一致性,证明了其在临床放射学中提高诊断效率和准确性的潜力.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 心血管成像 - 心血管成像
背景情况:
- 冠状动脉评分 (CACS) 对心血管风险评估至关重要.
- 评估CACS传统上涉及专家阅读者的半自动方法.
- 人工智能 (AI) 的整合提供了提高效率和准确性的潜力.
研究的目的:
- 评估AI和半自动CACS评估之间的相关性和一致性.
- 评估AI在基于CACS的心血管风险分类方面的表现.
- 确定AI在CACS解释中的临床相关性.
主要方法:
- 来自瑞典心肺生物图像研究 (SCAPIS) 的5057个CACS检查的分析.
- 人工智能评估与专家半自动CACS结果的比较.
- 统计方法包括皮尔森相关性,ICC,布兰德-阿尔特曼分析和加权卡帕分析.
主要成果:
- 观察到Agatston,体积和质量得分的良好相关性和一致性 (ICC > 0.99).
- 对病变数量达成高度一致 (ICC = 0.960).
- 权重卡帕分析显示,对心血管风险分类的高一致性 (0.913) 和准确性 (91.2%) 是很高的.
结论:
- 人工智能显示出与半自动CACS评估的良好相关性和一致性.
- 人工智能准确地对心血管风险进行了分类,显示出与既有方法的高度一致.
- 人工智能作为一种提高临床实践中CACS评估效率和准确性的工具,显示出显著的希望.
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