Generative Adversarial Networks in Cardiology.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Medical Imaging

Background:

• Generative adversarial networks (GANs) are advanced neural network models.
• GANs significantly enhance synthetic data quality, establishing them as a standard for data generation.
• Applications in cardiology are rapidly expanding.

Purpose of the Study:

• To summarize GAN applications in cardiology.
• To discuss the utility of GAN-generated data in research, clinical care, and academia.
• To present examples of GAN-generated cardiac imaging and explore future potential.

Main Methods:

• Review of GAN applications in cardiology.
• Generation and presentation of synthetic cardiac magnetic resonance and echocardiography images using 6 different GAN models.
• Discussion of GAN utility, future applications, and challenges.

Main Results:

• GANs generate highly realistic synthetic cardiac images, electrocardiography signals, and electronic health records.
• Image quality of GAN-generated cardiac magnetic resonance and echocardiography has evolved, becoming nearly indistinguishable from real images.
• GAN-generated data demonstrates utility across research, clinical care, and academic settings.

Conclusions:

• GANs offer significant potential for advancing cardiology through realistic data synthesis.
• Future applications include modality translation and patient trajectory modeling.
• Challenges in training dynamics, medical fidelity, and ethical considerations must be addressed for clinical integration.