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Heart failure remains a major public health challenge with growing costs.Ejection fraction (EF) is a key metric for the diagnosis and management ofheart failure however estimation of EF using echocardiography remains expensivefor the healthcare system and subject to intra/inter operator variability.While chest x-rays (CXR) are quick, inexpensive, and require less expertise,they do not provide sufficient information to the human eye to estimate EF.This work explores the efficacy of computer vision techniques to predictreduced EF solely from CXRs. We studied a dataset of 3488 CXRs from the MIMICCXR-jpg (MCR) dataset. Our work establishes benchmarks using multiplestate-of-the-art convolutional neural network architectures. The subsequentanalysis shows increasing model sizes from 8M to 23M parameters improvedclassification performance without overfitting the dataset. We further show howdata augmentation techniques such as CXR rotation and random cropping furtherimproves model performance another ~5%. Finally, we conduct an error analysisusing saliency maps and Grad-CAMs to better understand the failure modes ofconvolutional models on this task.

Predicting Ejection Fraction from Chest X-rays Using Computer Vision for Diagnosing Heart Failure