Key result
Artificial intelligence and deep learning models achieved diagnostic accuracies exceeding 90% for detecting hypertrophic cardiomyopathy across echocardiography, cardiac magnetic resonance, and electrocardiography.
Why the study?
HCM presents complex diagnostic, prognostic, and management challenges due to phenotypic heterogeneity and clinical course, while AI applications face challenges like data standardization, model interpretability, and privacy.
Answers are grounded in this paper and cite their sources.
This review highlights the transformative potential of artificial intelligence in integrating multimodal imaging and clinical data to enhance the diagnosis, phenotypic differentiation, and personalized management of hypertrophic cardiomyopathy.
Hypertrophic cardiomyopathy (HCM) is a common cardiovascular disease and one of the leading causes of exercise-induced sudden cardiac death in adolescents. HCM presents complex diagnostic, prognostic, and management challenges due to the phenotypic heterogeneity and clinical course. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies are expected to transform the roles of echocardiography, electrocardiography (ECG), and cardiac magnetic resonance (CMR) imaging in the clinical management of HCM. AI methods can fully integrate clinical and imaging data to enable a comprehensive assessment of the risk profile of a patient. However, challenges remain, such as insufficient data standardization across multiple sources, limited model interpretability, and data privacy issues. Despite these challenges, AI-based approaches have the potential to revolutionize the management of HCM by providing timely, accurate diagnoses and personalized treatment strategies based on individual patient risk profiles. This review systematically examines the current landscape of AI applications in HCM data analytics, with a focus on methodological advancements and clinical implementations. Furthermore, this review aims to facilitate the transition from experience-based to data-driven paradigms in HCM diagnosis, thereby advancing precision medicine and individualized patient management.
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Ma et al. (2026) conducted a review in Hypertrophic Cardiomyopathy. Artificial Intelligence was evaluated. Artificial intelligence and deep learning models achieved diagnostic accuracies exceeding 90% for detecting hypertrophic cardiomyopathy across echocardiography, cardiac magnetic resonance, and electrocardiography.
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