人工智能衍生的冠状动脉评分节省了时间,并且在常规心电图-通道CT上实现了接近放射科医生水平的精度
Jordan H Chamberlin1, Sameer Abrol1, James Munford1
1Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA.
The international journal of cardiovascular imaging
|December 16, 2024
概括
人工智能 (AI) 显著提高了冠状动脉评分 (CACS) 的效率,以优异的准确性将审查时间减少了60%. 建议采用半自动AI方法来进行最佳的临床风险评估.
科学领域:
- 心脏病学 心脏病学
- 放射学 放射学是一门学科.
- 医疗成像医学成像
背景情况:
- 人工智能 (AI) 在冠状动脉评分 (CACS) 中增强工作流程方面表现有前途.
- 以前的研究并没有同时证明使用人工智能用于CACS的效率,准确性和临床稳定性有所改善.
- 常规的评分计算机断层扫描 (CACS) 对心血管风险评估至关重要.
研究的目的:
- 与手动方法相比,评估用于CACS的AI模型的效率和准确性.
- 评估使用人工智能在CACS中的临床稳定性和改善工作流程的潜力.
- 为了确定人工智能对MESA (动脉样硬化多民族研究) 百分位分层的影响.
主要方法:
- 对148名接受常规CACSCT扫描的患者进行了回顾性分析.
- 手动 (仅专家),半自动 (AI + 专家审查) 和自动 (仅AI) CACS方法的比较.
- 主要终点:完成CACS的时间和类内相关系数 (ICC). 二级终点:MESA百分数和严重程度分层的差异.
主要成果:
- 人工智能和专家CACS之间的优秀协议 (ICC = 0.951).
- 自动AI CACS比半自动 (38秒) 和手动 (45秒) 方法 (15秒) 快得多 (P < 0.001).
- 人工智能在Agatston和MESA分数中表现出极好的准确性和低偏差,尽管建议采用半自动方法来最大限度地减少错误.
结论:
- 人工智能显著提高了CACS工作流程的效率,节省了大约60%的审查时间.
- 由人工智能驱动的CACS实现了卓越的准确性和与专家解释的良好一致性.
- 一种半自动的人工智能方法平衡了效率和准确性,建议尽量减少临床风险概况中的错误.
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