Deep Learning For Prediction of Fractional Flow Reserve From Resting Coronary Pressure Curves (ARTIST study).

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Abstract

It would be ideal for a non-hyperemic index to predict fractional flow reserve (FFR) more accurately, given FFR’s extensive validation in a multitude of clinical settings. The aim of this study was to derive a novel non-hyperemic algorithm based on deep learning and to validate it in an internal validation cohort against FFR. Methods and Results The ARTIST study is a post hoc analysis of 3 previously published studies. In a derivation cohort (random 80% sample of the total cohort) a deep neural network was trained with paired examples of resting coronary pressure curves and their FFR values. The resulting algorithm was validated against unseen resting pressure curves from a random 20% sample of the total cohort. The primary endpoint was diagnostic accuracy of the deep learning-derived algorithms against binary FFR≤0.8. A total of 1666 patients were included. Diagnostic accuracy of our convolutional neural network (CNN) and recurrent neural networks (RNN) against binary FFR≤0.80 were 79.6±1.9%, and 77.6±2.3%, respectively. Conclusions In the first study using a deep learning-based algorithm to predict FFR from resting coronary pressure curves, we did not find a clinically relevant increase in diagnostic accuracy versus non-hyperemic pressure ratios.