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

Deep learning (DL) continues to advance cardiac image analysis with increasingly sophisticated methodologies. Although convolutional neural networks laid the foundation for DL, emerging methods including graph neural networks, transformers, implicit neural representations, generative adversarial networks, and foundation models enable enhanced anatomical and functional modeling, image generation, and multimodal integration. Graph neural networks enable non-Euclidean data representations that preserve anatomical structure; transformers improve sequence modeling in dynamic imaging; and implicit neural representations introduce continuous spatial representations for more accurate reconstructions. Generative adversarial networks enhance image generation, noise reduction, and cross-modality synthesis adaptation, while foundation models introduce a unified, generalizable framework capable of adapting across diverse imaging tasks. This review discusses these key innovations of DL in cardiac imaging, their implications, and their challenges as well as potential future directions in the field, such as clinical validation trials.