Recovering Diagnostic Value: Super-Resolution-Aided Echocardiographic Classification in Resource-Constrained Imaging

TL;DR

This study demonstrates that deep learning-based super-resolution techniques can significantly improve the diagnostic accuracy of low-quality echocardiographic images, enhancing AI-assisted cardiac diagnosis in resource-limited settings.

Contribution

It explores the application of super-resolution models, SRGAN and SRResNet, to improve classification accuracy of poor-quality echocardiograms, a novel approach in this modality.

Findings

SRResNet outperforms SRGAN in accuracy and efficiency

Super-resolution enhances diagnostic classification in degraded echocardiograms

Improved image quality leads to better AI-based cardiac assessments

Abstract

Automated cardiac interpretation in resource-constrained settings (RCS) is often hindered by poor-quality echocardiographic imaging, limiting the effectiveness of downstream diagnostic models. While super-resolution (SR) techniques have shown promise in enhancing magnetic resonance imaging (MRI) and computed tomography (CT) scans, their application to echocardiography-a widely accessible but noise-prone modality-remains underexplored. In this work, we investigate the potential of deep learning-based SR to improve classification accuracy on low-quality 2D echocardiograms. Using the publicly available CAMUS dataset, we stratify samples by image quality and evaluate two clinically relevant tasks of varying complexity: a relatively simple Two-Chamber vs. Four-Chamber (2CH vs. 4CH) view classification and a more complex End-Diastole vs. End-Systole (ED vs. ES) phase classification. We apply two widely used SR models-Super-Resolution Generative Adversarial Network (SRGAN) and Super-Resolution Residual Network (SRResNet), to enhance poor-quality images and observe significant gains in performance metric-particularly with SRResNet, which also offers computational efficiency. Our findings demonstrate that SR can effectively recover diagnostic value in degraded echo scans, making it a viable tool for AI-assisted care in RCS, achieving more with less.