Deep learning-based prediction of fractional flow reserve after invasive coronary artery treatment

Invasive treatment of coronary artery disease (CAD) is costly and burdensome for the patient. Therefore, prediction of treatment success would be of clinical value. This study presents a deep learning method for the prediction of the post-treatment fractional flow reserve (FFR) in patients with coronary artery disease (CAD) from pre-treatment coronary CT angiography (CCTA). To simulate post-treatment FFR, pre-treatment coronary artery characteristics are modified to mimic invasive coronary treatment. Artery characterization and subsequent prediction of the FFR values along the artery are performed using deep learning. The method was tested on CCTA scans of 29 patients with invasive pre- and post-treatment FFR measurements along the artery. Achieved accuracy for the prediction of the presence of a functionally significant stenosis was 0.77 before and 0.63 after simulated treatment. The analysis took 0.8 s per artery. The results indicate that real-time treatment planning might be feasible.