Key result
Fully convolutional networks achieved a mean Dice score of 0.92 for cardiac MRI segmentation, while convolutional neural networks achieved 95.4% accuracy for cardiovascular disease classification.
Why the study?
Cardiovascular diseases remain the leading global cause of death, motivating the investigation of deep learning methods to improve accuracy and automation in cardiac MRI analysis.
Deep learning models, particularly FCNs and CNNs, significantly improve the accuracy and automation of cardiac MRI segmentation and classification, though challenges in data heterogeneity and generalizability remain.
AI-enhanced cardiac MRI may aid early CVD diagnosis; leaves open prospective validation before clinical adoption.
Despite improvements, cardiovascular diseases (CVD) remain the most significant killer globally, accounting for around 17.9 million lives annually. Advancement of cardiac imaging modalities has taken place with Magnetic Resonance Imaging (MRI) along with artificial intelligence (AI) for changing scenarios of early diagnosis and management in cardiovascular diseases. This work investigates the role and contribution of deep learning, especially Fully Convolutional Networks (FCNs) and Convolutional Neural Networks (CNNs), toward the improvement of accuracy and automation in cardiac MRI analysis. The integration of AI enables accurate segmentation, efficient clinical workflows, and scalable solutions for resource-limited environments. A review of publicly available datasets underlines challenges in data variability and generalizability and points to the need for standardized models and explainable AI approaches. This work, therefore, underlines the possibility of improved diagnostic efficiency and equity in healthcare delivery using AI-driven methodologies in cardiovascular diagnostics. Future directions will focus on refining model scalability, enhancing dataset diversity, and validating clinical applications to foster robust and adaptable solutions.
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Najdavan A. Kako (2025) conducted a review in Cardiovascular diseases. Deep learning models (Fully Convolutional Networks and Convolutional Neural Networks) was evaluated on Diagnostic accuracy and segmentation precision (Dice score). Fully convolutional networks achieved a mean Dice score of 0.92 for cardiac MRI segmentation, while convolutional neural networks achieved 95.4% accuracy for cardiovascular disease classification.
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