Machine learning for predicting fractional flow reserve based on optical coherence tomography in intermediate coronary stenosis

Abstract Background Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been previously investigated. The objective of the study was to evaluate a machine learning method to estimate FFR based on intravascular OCT images in intermediate coronary lesions. Methods Data from both OCT- and wire-based FFR methods were obtained for lesions of the left anterior descending artery in 125 patients. Based on the total number of lesions, training and testing groups were partitioned at a ratio of 5:1. For the training group, 36 features, including 16 clinical and lesion characteristics, and 21 OCT features, were used to model machine learning-FFR. machine learning-FFR values were then derived for the testing group and compared with wire-based FFR values in terms of a diagnosis of ischemia (FFR