Cardiovascular Magnetic Resonance imaging analysis using neural networks

Cardiovascular Magnetic Resonance (CMR) provides high-resolution images for diagnosing heart diseases, but interpreting these images is time-consuming and requires expert skill. Recently, deep neural networks have shown potential to automate and standardize CMR analysis, performing tasks such as image sorting, cardiac chamber segmentation, and tissue characterization with speed and precision. This review summarizes the theoretical foundations of neural networks applied to CMR, evaluates state-of-the-art deep learning architectures (e.g., convolutional and recurrent networks, U-Net variants) for various CMR tasks, and highlights key performance metrics and validation strategies. We incorporate a comprehensive survey of recent literature and discuss critical challenges such as the curse of dimensionality, limited annotated datasets, and model generalizability across imaging centers. We also address issues of bias and fairness in algorithms, the impact of data variability and annotation quality, and failure modes in deployment. In addition, we examine regulatory and practical considerations for clinical integration—covering FDA/CE approvals, patient safety, economic factors, and workflow integration—and outline future directions including hybrid physics-informed approaches and deployment science. The review is focused on a core clinical application (automated CMR image segmentation and classification) to provide depth of analysis. Our findings indicate that while neural networks can markedly improve the efficiency and reproducibility of CMR analysis, careful attention to technical and clinical validation is required to translate these advances into safe, effective real-world tools.