Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications

Area of Science:

• Cardiovascular imaging and diagnostics
• Medical Artificial Intelligence (AI)

Background:

• Cardiac wall motion abnormalities are key indicators of cardiovascular risk, necessitating accurate detection.
• Conventional imaging methods (echocardiography, MRI, CT) have limitations in cost, accessibility, and spatiotemporal analysis.
• Machine learning (ML), especially deep learning (DL), offers automated feature extraction for improved cardiac motion analysis.

Purpose of the Study:

• To review state-of-the-art deep learning methods for predicting cardiac wall motion abnormalities.
• To emphasize DL applications across various imaging modalities like echocardiography, 4D CT, and cine MRI.
• To discuss the potential and challenges of ML in clinical practice for cardiovascular care.

Main Methods:

• Review of recent studies utilizing deep learning techniques (CNNs, RNNs, Transformers) for cardiac wall motion analysis.
• Focus on DL applications in echocardiography, 4D CT, and cine MRI datasets.
• Analysis of performance metrics and comparison with expert clinicians.

Main Results:

• Deep learning models demonstrate potential for accurate segmentation, motion estimation, and abnormality detection.
• Performance of DL approaches can be comparable to that of expert clinicians.
• Identified challenges include data scarcity, model interpretability, and limited external validation.

Conclusions:

• Deep learning holds significant promise for enhancing the detection and prediction of cardiac wall motion abnormalities.
• Addressing challenges in data availability and model transparency is crucial for clinical translation.
• Integrating advanced imaging with ML frameworks can lead to reliable cardiac simulators for personalized treatment planning and improved cardiovascular outcomes.