使用人工智能进行内血管动脉瘤修复后监测的体积测量
Olivier L R M van Tongeren1, Alexander Vanmaele2, Vinamr Rastogi1
1Department of Vascular Surgery, Erasmus University Medical Centre, Rotterdam, the Netherlands.
概括
人工智能软件准确地测量腹腔大动脉动脉瘤囊体积演变在内血管动脉瘤修复后,改善风险分层. 这种人工智能工具提供了一致和可靠的监控,潜在地增强了个性化的患者护理.
科学领域:
- 血管外科 血管外科
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 由于测量变化,目前对腹腔大动脉瘤 (AAA) 囊大小在内血管动脉瘤修复 (EVAR) 后的监测不足于最佳.
- 动脉瘤囊体积测量是一个比直径更敏感的变化指标,但耗时.
- 这项研究调查了人工智能驱动的软件对EVAR后AAA袋体积测量的准确性和一致性.
研究的目的:
- 为了评估AI驱动软件PRAEVAorta 2的准确性和一致性,与用于测量AAA袋体积的半自动细分方法相比.
- 评估两种方法对AAA囊进化 (回归,稳定,生长) 的临床影响.
- 为了比较体积和演变测量的方法间和方法内部协议.
主要方法:
- 在49名EVAR后患者中,通过AI驱动 (PRAEVAorta 2) 和半自动 (3mensio) 的AAA囊体积测量从计算机断层扫描血管学 (CTA) 中进行的比较.
- 分析的重点是30天和一年的随访期间的绝对体积和体积演变.
- 使用布兰德-阿尔特曼分析,类内相关系数 (ICC) 和科恩的 κ统计学来评估方法间和方法内部的协议.
主要成果:
- 人工智能和半自动化方法之间对动脉瘤囊体积测量有很好的相关性 (ICC = 0.94),对体积演变有很好至很好的相关性 (ICC = 0.85).
- 公平相关性 (科恩 κ = 0.33) 对于AAA袋进化分类,有24%的差异.
- 人工智能软件表现出完美的方法内部一致性 (偏差 = -0.01 cc),显示出比半自动化方法更高的可靠性.
结论:
- 由人工智能驱动的软件准确地以高的一致性测量AAA袋体积演变,有可能改善EVAR后的风险分层.
- 人工智能驱动的AAA袋进化的分类可能会增强个性化的监控程序.
- 高质量的CTA图像至关重要;通过非对比CT扫描验证AI软件可以减少辐射暴露.
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