Key result
Deep learning automates analysis across five cardiovascular imaging modalities but faces bias and interpretability barriers.
Why the study?
Conventional cardiovascular image interpretation is limited by operator dependence, variable image quality, and time-consuming analysis.
Deep learning offers automated and reproducible analysis of cardiovascular images, though clinical deployment remains hindered by dataset biases, domain shifts, and limited interpretability.
May advance automated cardiovascular imaging analysis; leaves open clinical translation pending bias and interpretability solutions.
Cardiovascular diseases (CVDs) remain the leading cause of global mortality and impose a substantial clinical and socioeconomic burden. Medical imaging plays a central role in assessing cardiac structure, function, and tissue characteristics. However, conventional image interpretation is limited by operator dependence, variable image quality, and time-consuming analysis. Deep learning (DL) enables automated, efficient, and reproducible analysis of cardiovascular images and has become an important tool in this domain. This review presents a comprehensive and modality-oriented synthesis of DL applications across five major cardiovascular imaging modalities: echocardiography, coronary CT angiography (CCTA), cardiac magnetic resonance imaging (CMRI), nuclear imaging (SPECT/PET), and X-ray angiography. We review the main DL architectures, including convolutional neural networks, recurrent models, transformer-based networks, graph-based models, hybrid frameworks, and generative models. Their performance is examined across core tasks such as segmentation, classification, detection, motion analysis, and functional assessment. The strengths and limitations of these approaches are summarized for each modality. Beyond modality-specific studies, evidence is integrated to identify cross-modality methodological trends, transferable design principles, and persistent technical gaps. Major barriers to clinical deployment are discussed, including dataset bias and imbalance, annotation variability, imaging artifacts, domain shift, limited multimodal integration, high computational cost, and limited interpretability. Future research directions are outlined, with emphasis on CNN–transformer hybrids, diffusion models, multimodal foundation models, federated learning, and explainable artificial intelligence. This review aims to support the development of robust and clinically reliable DL systems and to facilitate their translation into routine cardiovascular imaging practice.
No takes yet. Share an insight, caveat, or question.
Haq et al. (2026) conducted a review in Cardiovascular diseases (CVDs). Deep learning (DL) was evaluated. Deep learning enables automated and reproducible analysis across five major cardiovascular imaging modalities, though clinical deployment faces barriers like dataset bias and limited interpretability.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: