Predicting Cardiac Magnetic Resonance-Derived Ejection Fraction from Echocardiogram Via Deep Learning Approach in Tetralogy of Fallot.

Abstract

Systolic function assessment is essential in children with congenital heart disease. Traditional methods of echocardiographic left ventricular ejection fraction (LVEF) estimation might overestimate systolic function compared to the gold standard of cardiac magnetic resonance imaging (CMR), especially in Tetralogy of Fallot (TOF). Deep learning technologies such as EchoNet-Dynamic offer more consistent cardiac evaluations and can potentially accurately predict LVEF using echocardiographic videos. The EchoNet-Dynamic/EchoNet-Peds models predict LVEF using echocardiograms with expert-measured LVEF as the ground truth. Using a transfer learning approach, we fine-tuned this model to predict LVEF with CMR-derived LVEF as ground truth and TOF echocardiograms as input images. For echocardiograms in the PSAX view, the model predicted CMR LVEF with an R2 of 0.79 and an MAE of 4.41. For the A4C view, the model predicted CMR LVEF with an R2 of 0.53 and an MAE of 6.4. Plotted ROC curves indicate that both tuned models differentiated well between normal and reduced LVEF. This study shows the potential of Convolutional Neural Network (CNN) models in transforming the field of cardiac imaging interpretation via a hybrid approach using the CMR labels and echocardiogram videos offering advancements over conventional methods.