Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK.
Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK.
Medicina (Kaunas). 2024 Oct 3;60(10):1618. doi: 10.3390/medicina60101618.
(1) : Flow assessment using cardiovascular magnetic resonance (CMR) provides important implications in determining physiologic parameters and clinically important markers. However, post-processing of CMR images remains labor- and time-intensive. This study aims to assess the validity and repeatability of fully automated segmentation of phase contrast velocity-encoded aortic root plane. (2) Aortic root images from 125 patients are segmented by artificial intelligence (AI), developed using convolutional neural networks and trained with a multicentre cohort of 160 subjects. Derived simple flow indices (forward and backward flow, systolic flow and velocity) and complex indices (aortic maximum area, systolic flow reversal ratio, flow displacement, and its angle change) were compared with those derived from manual contours. (3) : AI-derived simple flow indices yielded excellent repeatability compared to human segmentation ( < 0.001), with an insignificant level of bias. Complex flow indices feature good to excellent repeatability ( < 0.001), with insignificant levels of bias except flow displacement angle change and systolic retrograde flow yielding significant levels of bias ( < 0.001 and < 0.05, respectively). (4) : Automated flow quantification using aortic root images is comparable to human segmentation and has good to excellent repeatability. However, flow helicity and systolic retrograde flow are associated with a significant level of bias. Overall, all parameters show clinical repeatability.
(1) ：使用心血管磁共振（CMR）进行流量评估在确定生理参数和临床重要标志物方面具有重要意义。然而，CMR 图像的后处理仍然是劳动密集型和时间密集型的。本研究旨在评估完全自动化分割相位对比速度编码主动脉根部平面的有效性和可重复性。(2) ：使用人工神经网络开发的人工智能（AI）对 125 名患者的主动脉根部图像进行分割，并使用 160 名患者的多中心队列进行训练。得出的简单流量指数（前向和后向流量、收缩期流量和速度）和复杂指数（主动脉最大面积、收缩期流量反转比、流量位移及其角度变化）与手动轮廓得出的指数进行了比较。(3) ：与人工分割相比，AI 衍生的简单流量指数具有出色的可重复性（<0.001），且偏差不显著。复杂流量指数具有良好到极好的可重复性（<0.001），且偏差不显著，除流量位移角度变化和收缩期反向流量外，均有显著的偏差（分别为<0.001 和<0.05）。(4) ：使用主动脉根部图像进行自动流量定量与人工分割相当，具有良好到极好的可重复性。然而，流量螺旋度和收缩期反向流量与显著的偏差相关。总体而言，所有参数均显示出临床可重复性。
