在TAVI之前,人工智能辅助的计算机断层扫描分析用于程序前规划
Mani Arsalan1,2, Hanna Schneider3, Kerstin Piayda4
1Department of Cardiac Surgery, University Hospital of the Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany. mani.arsalan@gmail.com.
概括
一种用于心脏CT分析的新型深度学习算法与跨导管大动脉植入 (TAVI) 规划的标准方法有很好的相关性. 这种人工智能 (AI) 工具为手术前CT测量提供了一个有希望的,自动化的替代方案.
科学领域:
- 心血管成像和干预方法
- 人工智能在医学中的应用
- 医疗设备软件 医疗设备软件
背景情况:
- 目前用于心脏CT分析的软件在过导管大动脉植入前 (TAVI) 需要手动输入,无法独立执行完整的分析.
- 需要自动化,高效和准确的程序前CT分析工具来帮助TAVI规划.
研究的目的:
- 评估基于深度学习的全自动化算法的性能,用于TAVI患者的手术前CT分析.
- 将人工智能算法测量的准确性和相关性与当前临床标准软件进行比较.
主要方法:
- 一个回顾性分析的手术前CT数据集从98名患者接受TAVI重症大动脉狭窄.
- 从标准的TAVI CT分析软件和基于深度学习的完全自动化的CT分析平台获得的测量 (环形直径,周长,面积和冠状动脉距离) 的比较.
主要成果:
- 人工智能和传统方法之间的圆环直径,周长和面积测量观察到良好的相关性 (类内相关系数>0.95),平均绝对误差低.
- 对于从年环到左侧和右侧冠状动脉的距离发现了良好的相关性,这表明了可靠的解剖评估.
- 基于人工智能的分析显示了高准确度,平均绝对百分比错误的圆环直径,周长和面积分别为2.6%,2.9%和4.8%.
结论:
- 基于深度学习的CT分析表明与关键的程序前TAVI测量的传统评估有良好至优异的相关性.
- 完全自动化的基于人工智能的CT分析是TAVI程序前规划的有价值和潜在更高效的替代方案.
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