Prediction of FFR from IVUS Images Using Machine Learning

We present a machine learning approach for predicting fractional flow reserve (FFR) from intrayscular ultrasound images (IVUS) in coronary arteries. IVUS images and FFR measurements were collected from 1744 patients and 1447 lumen and plaque segmentation masks were generated from 1447 IVUS images using an automatic segmentation model trained on separate 70 IVUS images and minor manual corrections. Using total 114 features from the masks and general patient informarion, we trained random forest (RF), extreme gradient boost (XGBoost) and artificial neural network (ANN) models for a binary classification of FFR-80 threshold (FFR < 0.8 v.s. FFR > 0.8) for comparison. The ensembled XGBoost models evaluated in 290 unseen cases achieved 81% accuracy and 70% recall.