Key result
. No new claims.
Why the study?
Traditional imaging modalities for cardiac wall motion analysis face limitations in accessibility, cost, and complex spatiotemporal analysis, prompting evaluation of deep learning approaches.
Do deep learning methods improve the accuracy and automation of cardiac wall motion analysis across imaging modalities?
Do deep learning methods improve the accuracy and automation of cardiac wall motion analysis across imaging modalities?
Deep learning techniques offer automated, expert-level accuracy for cardiac wall motion analysis across various imaging modalities, paving the way for personalized treatment planning.
May expand access to accurate wall motion assessment beyond traditional imaging; leaves open outcome impact pending prospective trials.
Abnormalities in cardiac wall motion are strong predictors of cardiovascular risk, making their accurate detection essential for early diagnosis and effective clinical management. Traditional imaging modalities such as echocardiography, magnetic resonance imaging (MRI), and computed tomography (CT) provide valuable insights but face limitations related to accessibility, cost, and the complexity of spatiotemporal analysis. Recent advances in machine learning (ML), particularly deep learning (DL), have enabled automated extraction of spatial and temporal features from medical imaging. They improved accuracy in segmentation, motion estimation, and detection of regional wall motion abnormalities. This paper reviews state-of-the-art methods for predicting cardiac wall motion, with emphasis on DL applications across echocardiography, 4D CT, and cine MRI datasets. Representative studies demonstrate the potential of convolutional neural networks, recurrent neural networks, and transformers to achieve performance comparable to expert clinicians, while also highlighting challenges such as data scarcity, model interpretability, and limited external validation. Addressing these issues will be critical for translating ML-based approaches into routine practice, and integration of advanced imaging with robust ML frameworks helps in developing a reliable cardiac wall motion simulators for personalized treatment planning and improved cardiovascular care.
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Monfared et al. (2026) conducted a review in Cardiac wall motion abnormalities. Deep learning (DL) methods vs. Expert clinicians was evaluated. Deep learning methods achieve performance comparable to expert clinicians in predicting cardiac wall motion abnormalities across echocardiography, 4D CT, and cine MRI datasets.
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