计算机断层扫描切片厚度对基于深度学习的自动冠状动脉评分软件性能的影响
Suh Young Kim1,2, Young Joo Suh3, Hye-Jeong Lee3
1Department of Radiology, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea.
Quantitative imaging in medicine and surgery
|July 17, 2023
概括
深度学习冠状动脉评分软件与手动评分有很好的一致性,无论CT切片的厚度如何. 然而,3毫米切片提供了稍微更好的准确性来分类的严重性.
科学领域:
- 放射学 放射学是指放射学
- 人工智能的人工智能
- 心血管成像 - 心血管成像
背景情况:
- 计算机断层扫描 (CT) 切片厚度对基于深度学习 (DL) 的冠状动脉 (CAC) 评分的影响未被检查.
- CAC评分对于评估心血管风险至关重要.
研究的目的:
- 评估CT切片厚度 (1.5毫米与3毫米) 对基于DL的自动CAC评分准确度的影响.
- 为了将DL软件性能与手动评分进行比较,作为参考标准.
主要方法:
- 追溯分析了844名接受了ECG导入的CAC得分CT扫描的患者.
- 自动CAC评分使用DL (3D U-Net) 和手动评分作为参考.
- 类内相关系数 (ICC) 和加权的卡帕 (κ) 统计数据评估了可靠性和一致性.
主要成果:
- 对于1.5毫米 (ICC 0.982) 和3毫米 (ICC 0.969) 的切片,自动和手动CAC评分之间非常一致.
- 对于CAC严重程度的绝佳分类一致,对于3毫米扫描的优异结果 (加权 κ 0.961与0.851).
结论:
- 基于DL的自动CAC评分显示了高准确度,无论CT切片的厚度如何.
- 与1.5毫米切片相比,3毫米切片厚度为CAC严重性分类提供了稍微更好的协议.
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