Real time prediction of instantaneous wave-free ratio based on lumped parameter and neural network models

The Instantaneous wave-Free Ratio (iFR) is considered a potential alternative to Fractional Flow Reserve (FFR) in the diagnosis of coronary artery disease, as it does not require vasodilator administration. Nonetheless, its clinical use is similarly restricted by the need for invasive measurement. Therefore, there is an urgent need to develop a non-invasive method for calculating iFR, in order to enable rapid and non-invasive diagnosis of myocardial ischemia.

We propose a physical framework, termed the Lumped Parameter-Neural Network (LPNN), that combines a lumped parameter model (LPM) of coronary arteries with a fully connected neural network (NN)-based stenotic resistance model for accurate prediction of coronary artery resting flow and pressure, and apply it to noninvasive calculation of iFR (LPNN-iFR_CT). In addition, the principle of coronary compensation is also considered in the LPNN model to accurately obtain the resistance boundary conditions at resting, and the personalized aortic pressure and cardiac output are optimized by adding simulated annealing method, thereby saving calculation time. The accuracy of LPNN-iFR_CT was verified in 61 patients measured clinically.

LPNN-iFR_CT has a good correlation (r = 0.94) and consistency (bias:0.0, LOA: -0.05 to 0.05) with the measured iFR, and the calculation time for a cycle of specific patients is 6 s. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of LPNN-iFR_CT were: 85.71%, 93.94%, 92.31%, 88.57%, 90.16%. The area under curve of LPNN-iFR_CT was 0.933.

LPNN-iFR_CT demonstrates good diagnostic performance and short calculation time, proving clinical application value as an alternative non-invasively method for auxiliary preoperative diagnosis of myocardial ischemia.