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

TL;DR

This study demonstrates that deep learning models can predict ejection fraction from chest X-rays with improved accuracy through larger architectures and data augmentation, offering a cost-effective alternative for heart failure diagnosis.

Contribution

The paper introduces a novel approach using convolutional neural networks to estimate ejection fraction from CXRs, establishing benchmarks and analyzing model interpretability.

Findings

Larger CNN architectures improve prediction accuracy.

Data augmentation enhances model performance by ~5%.

Saliency maps reveal model failure modes.

Abstract

Heart failure remains a major public health challenge with growing costs. Ejection fraction (EF) is a key metric for the diagnosis and management of heart failure however estimation of EF using echocardiography remains expensive for 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 predict reduced EF solely from CXRs. We studied a dataset of 3488 CXRs from the MIMIC CXR-jpg (MCR) dataset. Our work establishes benchmarks using multiple state-of-the-art convolutional neural network architectures. The subsequent analysis shows increasing model sizes from 8M to 23M parameters improved classification performance without overfitting the dataset. We further show how data augmentation techniques such as CXR rotation and random cropping further improves model performance another ~5%. Finally, we conduct an error analysis using saliency maps and Grad-CAMs to better understand the failure modes of convolutional models on this task.