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Deep Learning for Cardiac Image Analysis: Unveiling Advances in Deep Learning Architectures.

Joske L van der Zande¹, Laura Alvarez-Florez², Rick H J A Volleberg³

¹Department of Cardiology, Radboud University Medical Center, Nijmegen, the Netherlands; Diagnostic Image Analysis Group (DIAG), Radboud University Medical Center, Nijmegen, the Netherlands.

JACC. Cardiovascular Imaging

|May 1, 2026

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Summary

This summary is machine-generated.

Emerging deep learning methods like graph neural networks and transformers are revolutionizing cardiac image analysis. These advanced techniques enhance anatomical modeling, image generation, and multimodal integration for better cardiovascular insights.

Area of Science:

Cardiovascular Imaging
Artificial Intelligence
Medical Image Analysis

Background:

Deep learning (DL) has significantly advanced cardiac image analysis.
Convolutional neural networks (CNNs) were foundational, but newer methods offer greater capabilities.

Purpose of the Study:

To review key innovations in deep learning for cardiac imaging.
To discuss the implications, challenges, and future directions of these advanced DL methods.

Main Methods:

Review of emerging DL methodologies including graph neural networks (GNNs), transformers, implicit neural representations (INRs), generative adversarial networks (GANs), and foundation models.
Analysis of how these methods enhance anatomical and functional modeling, image generation, and multimodal integration.

Keywords:

artificial intelligence cardiac imaging deep learning foundation models generative adversarial networks graph neural networks implicit neural representations transformers

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Main Results:

GNNs enable non-Euclidean representations preserving anatomical structure.
Transformers improve sequence modeling for dynamic cardiac imaging.
INRs offer continuous spatial representations for accurate reconstructions.
GANs enhance image generation, noise reduction, and cross-modality synthesis.
Foundation models provide a unified, adaptable framework for diverse cardiac imaging tasks.

Conclusions:

Advanced DL methods offer significant improvements in cardiac image analysis.
Future directions include clinical validation trials and further integration of these technologies.