Key result
AI-empowered echocardiography improves differential diagnosis, phenotyping, and risk stratification in HCM.
Why the study?
Conventional echocardiography in HCM relies heavily on operator expertise and struggles to comprehensively extract latent information, while emerging AI methods offer automated, data-driven decision support.
Does artificial intelligence-empowered echocardiography improve the diagnosis and clinical management of hypertrophic cardiomyopathy?
Does artificial intelligence-empowered echocardiography improve the diagnosis and clinical management of hypertrophic cardiomyopathy?
AI-empowered echocardiography provides highly accurate, automated tools for the diagnosis and differential diagnosis of hypertrophic cardiomyopathy, though large-scale clinical validation is still needed.
Echocardiography remains central to HCM management; leaves open needs for refined phenotyping and risk stratification in heterogeneous disease.
Hypertrophic cardiomyopathy (HCM) is a common and highly heterogeneous inherited cardiomyopathy characterized by complex clinical phenotypes and diverse disease trajectories, posing significant challenges for early diagnosis, precise phenotypic classification, and risk stratification. Owing to its noninvasive nature, repeatability, and wide availability, echocardiography remains the cornerstone imaging modality for the diagnosis and longitudinal management of HCM. However, conventional echocardiographic analysis relies heavily on operator expertise and is limited in its ability to comprehensively extract latent structural, functional, and tissue-level information embedded within imaging data. In recent years, artificial intelligence (AI), particularly deep learning, has undergone rapid development in automated echocardiographic analysis, enabling a paradigm shift from traditional morphology-based assessment toward data-driven intelligent decision-support platforms. This review systematically categorizes AI-based methods in echocardiography according to the complexity of data processing, ranging from single-frame structural and texture analysis to spatiotemporal modeling of cardiac function, multi-view representation learning, and ultimately multimodal integration incorporating diverse clinical data sources. We further summarize the clinical applications of these AI-based methods in HCM, including diagnosis and differential diagnosis, phenotype characterization, and risk prediction. In addition, current challenges are discussed, including limited interpretability, data heterogeneity, and insufficient large-scale clinical validation, and future research directions are proposed. Overall, AI-empowered echocardiography holds substantial promise for advancing precision diagnosis, risk stratification, and personalized management of HCM, facilitating a transition toward more intelligent and individualized cardiovascular care.
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Zhang et al. (2026) conducted a review in Hypertrophic cardiomyopathy. Artificial intelligence-empowered echocardiography vs. Conventional echocardiographic analysis was evaluated. Artificial intelligence-empowered echocardiography facilitates accurate differential diagnosis, phenotypic characterization, and risk stratification in hypertrophic cardiomyopathy.
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