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Coronary Artery Disease (CAD) is one of the most common forms of heartdisease, which is caused by a buildup of atherosclerotic plaque (known asstenosis) in the coronary arteries, leading to insufficient supplement ofblood, oxygen, and nutrients to the heart. Fractional Flow Reserve (FFR),measuring the pressure ratio between the aorta and distal coronary artery, isan invasive physiologic gold standard for assessing the severity of coronaryartery stenosis. Despite its benefits, invasive FFR assessment is stillunderutilized due to its high cost, time-consuming, experimental variability,and increased risk to patients. In this study, an attention-basedmulti-fidelity machine learning model (AttMulFid) is proposed forcomputationally efficient and accurate FFR assessment with uncertaintymeasurement. Within AttMulFid, an autoencoder is utilized to intelligentlyselect geometric features from coronary arteries, with additional attention onthe key area. Results show that the geometric features are able to representthe entirety of the geometric information and intelligently allocate attentionbased on crucial properties of geometry. Furthermore, the AttMulFid is afeasible approach for non-invasive, rapid, and accurate FFR assessment (with0.002s/simulation).

Attention-based Multi-fidelity Machine Learning Model for Computational Fractional Flow Reserve Assessment