Itu Lucian, Rapaka Saikiran, Passerini Tiziano, Georgescu Bogdan, Schwemmer Chris, Schoebinger Max, Flohr Thomas, Sharma Puneet, Comaniciu Dorin
Corporate Technology, Siemens SRL, Brasov, Romania; Department of Automation and Information Technology, Transilvania University of Brasov, Brasov, Romania;
Medical Imaging Technologies, Siemens Healthcare, Princeton, New Jersey; and
J Appl Physiol (1985). 2016 Jul 1;121(1):42-52. doi: 10.1152/japplphysiol.00752.2015. Epub 2016 Apr 14.
Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor.
血流储备分数（FFR）是一种量化冠状动脉病变严重程度的功能指标，临床上通过基于导管的侵入性测量获得。最近，基于物理的模型在能够根据患者特定的解剖信息（例如从心脏和冠状动脉的计算机断层扫描中获得的信息）无创估计FFR方面显示出巨大潜力。然而，这些模型具有很高的计算需求，限制了它们在临床上的应用。在本文中，我们提出了一种基于机器学习的模型来预测FFR，作为基于物理方法的替代方案。该模型在一个由合成生成的冠状动脉解剖结构组成的大型数据库上进行训练，其中目标值使用基于物理的模型计算。训练后的模型预测冠状动脉树中心线上每个点的FFR，并通过将预测结果与基于物理的计算结果以及对总共87例患者和125个病变的侵入性测量的FFR进行比较来评估其性能。机器学习预测与基于物理的预测之间的相关性非常好（0.9994，P < 0.001），并且在Bland-Altman分析中未发现系统偏差：平均差异为-0.00081±0.0039。在125个病变中，有38个病变的侵入性FFR≤0.80，机器学习算法对其预测的灵敏度为81.6%，特异性为83.9%，准确率为83.2%。相关性为0.729（P < 0.001）。与基于物理的计算相比，平均执行时间减少了80倍以上，从而实现了对FFR的近实时评估。在配备3.4 GHz英特尔i7 8核处理器的工作站上，平均执行时间从CFD模型的196.3±78.5秒降至机器学习模型的约2.4±0.44秒。
