Predicting Ejection Fraction from Chest X-rays Using Computer Vision for Diagnosing Heart Failure
This work addresses the problem of expensive and variable echocardiography for heart failure diagnosis by providing a computer vision-based alternative using chest X-rays, though it is incremental as it builds on existing neural network architectures.
The paper tackled predicting ejection fraction from chest X-rays to diagnose heart failure, achieving a ~5% performance improvement with data augmentation and analyzing failure modes using saliency maps.
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.