Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients.

血流储备分数是目前评估冠心病狭窄对功能影响的参考标准。在这项研究中，将三种复杂程度不同的人工智能模型与分数流量储备测量值进行了比较。这三个模型是多元多项式回归，这是一种主要用于相关的统计方法；前馈神经网络；以及长短期记忆，这是一种适用于序列建模的循环神经网络。这些模型最初是使用从经过验证的基于物理的一维模型生成的虚拟患者数据库进行训练的。前馈神经网络在考虑的所有测试案例中表现最佳，这些案例是来自虚拟患者数据库的单个血管案例，来自虚拟患者数据库的多血管网络，以及来自真实患者的 25 个临床侵入性血流储备分数测量值。前馈神经网络模型在涉及虚拟患者的两项测试中均实现了约 99% 的诊断准确率，与有创分数流量储备测量相比，诊断准确率达到了可观的 72%。多元多项式回归模型在单血管案例中表现良好，但在网络案例中表现不佳，因为输入特征的变化要大得多。对于单血管病例，长短期记忆表现良好，但对于虚拟多血管病例和患者病例，往往偏向于正分数流量储备预测。全面的，