Deep Learning for Cardiovascular Disease Detection
A Review Based on Cardiac Magnetic Resonance Imaging Data
DOI:
https://doi.org/10.14500/aro.11971Keywords:
Cardiovascular diseases, Convolutional Neural Networks (CNN), Deep learning, Fully convolutional networks, Magnetic resonance imagingAbstract
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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