Key result
Deep learning techniques advance automated segmentation and identification of cardiac structures.
Why the study?
Recent advances in deep learning and modern cardiac imaging modalities have created new opportunities for automated disease identification, highlighting the need to review advanced architectures and model compression techniques.
Systematic Review
Advanced deep learning techniques, including transformers and foundation models, offer significant progress in the automated interpretation and segmentation of cardiac images.
May streamline cardiac image analysis in practice; reinforces evidence for transformers and foundation models in segmentation.
BACKGROUND: Cardiac image analysis plays an important role in detecting and categorizing cardiovascular diseases (CVDs), such as coronary artery disease (CAD), heart failure, congenital heart defects, arrhythmias (irregular heartbeat), and valvular heart disease. The automated identification of these diseases represents a significant advancement in achieving early diagnosis and mitigating disease exacerbations. While extant methodologies offer advanced means for the automatic segmentation and identification of cardiac structures and pathologies, recent strides in deep learning (DL) and modern imaging modalities within cardiology have introduced new opportunities for researchers. This has underscored the importance of deep model compression and optimization techniques. This review comprehensively surveys recent deep learning applications in interpreting cardiac images, encompassing common imaging modalities. METHOD: Following the PRISMA methodology, we reviewed recent research on advanced and frequently used DL architectures in cardiac image analysis, along with the most employed cardiac imaging datasets. Additionally, we analyse recent contributions focused on deep model compression in cardiac image processing tasks. RESULTS: The application of DL techniques in cardiac image analysis has seen significant progress through the introduction of new techniques such as transformers, foundation models and compression techniques. CONCLUSIONS: The paper concludes with a critical discussion addressing open challenges and proposing future research directions in this domain.
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Boukhamla et al. (2025) conducted a systematic review in Cardiovascular diseases. Deep learning techniques was evaluated. Deep learning techniques, including transformers, foundation models, and compression techniques, have shown significant progress in the automated segmentation and identification of cardiac structures.
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