Aug 2026· Frontiers in Cardiovascular Medicine· Vol 13· 0 citations· 94 references
Medicine
TL;DR
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.
Abstract
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.
It is emphasized that successful integration of AI into cardiovascular care requires rigorous prospective validation, transparent algorithmic governance, equitable data representation, and human-AI collaborative frameworks, provided its meaningful clinical implication is demonstrated through improved patient outcomes.
Xu Xia, Wasim Ullah Khan, Q. Khan et al.· Trends in cardiovascular med...· 0 citations
Pulmonary hypertension (PH) remains diagnostically challenging due to the associated non-specific symptomatology and frequent diagnostic delays, both of which contribute to increased morbidity and mortality. Meanwhile, right heart catheterization is the diagnostic gold standard; nonetheless, the invasive nature and limited accessibility of this technique limit its routine use, particularly in resource-constrained settings. This review evaluates computational approaches that may enhance PH diagnosis through advanced analysis of cardiovascular imaging data. We conducted a comprehensive literature review focusing on computer-assisted diagnostic methods in PH across multiple imaging modalities, including electrocardiogram, chest X-ray, echocardiography, cardiac magnetic resonance (CMR) imaging, and cardiac computed tomography (CCT). Eligible studies were analyzed for diagnostic performance, clinical applicability, and methodological rigor. Preliminary studies have demonstrated promising performance in detecting early or subclinical PH phenotypes across various imaging platforms. Advanced imaging modalities benefit from automated segmentation and quantitative analysis, and CMR- and CT-based approaches demonstrate high diagnostic accuracy. Current artificial intelligence (AI) models face significant challenges related to clinical interpretability, external validation across diverse populations, and seamless integration into existing diagnostic workflows. Most studies are based on single-center retrospective cohorts, underscoring the need for multicenter prospective validation. To address these challenges, future research must prioritize advancing methodological transparency through explainable AI (XAI) and ensuring data privacy via federated learning. Crucially, the next phase of innovation lies in synergistic multimodal fusion (MMF) architectures that synthesize heterogeneous data—ranging from imaging to hemodynamics—to enhance phenotypic precision. Furthermore, leveraging large language models (LLMs) for computational phenotyping from electronic health records offers a scalable solution for identifying undiagnosed patients. Finally, realizing clinical translation requires rigorous multicenter prospective validation and seamless integration into existing workflows to ensure these tools effectively support decision-making in real-world practice. While computational approaches in PH diagnostics show promise for improving early detection and diagnostic accuracy, significant challenges remain before widespread clinical adoption. Future development should prioritize multicenter validation, standardized frameworks integrating multi-parametric imaging with clinical biomarkers, and transparent methodologies to support clinical decision-making. The integration of these computational tools into conventional diagnostic pathways may enhance PH management through earlier detection and more precise risk stratification.
Ze-Chen Li, Xiao-Wei Xu, Jia-Hong Li et al.· Reviews in cardiovascular me...· 0 citations
Heart failure (HF) is increasingly understood not as a single, uniformly treated diagnosis but as a heterogeneous syndrome requiring aetiological clarification, in which cardiac imaging is central. As the opening article of this journal's ‘Imaging in Heart Failure’ section, this review surveys the technologies currently reshaping HF imaging and sets out the section's scope and priorities, framing the shift from a descriptive, modality-siloed practice toward an integrated, predictive, patient-specific discipline. Artificial intelligence (AI) now delivers expert-level echocardiography automation, guides image acquisition by novices in resource-limited settings, detects aetiologies such as transthyretin amyloid cardiomyopathy from a single acquisition and enables deep phenotyping through radiomics and vendor-agnostic strain analysis. Handheld, AI-enabled point-of-care ultrasound extends imaging-guided triage beyond the echocardiography laboratory. Cardiovascular magnetic resonance (CMR) advances — parametric mapping, four-dimensional flow, diffusion tensor imaging, spectroscopy, and accelerated reconstruction — broaden tissue and metabolic characterisation, including patients with implanted devices. Molecular imaging with novel positron emission tomography tracers and hyperpolarised magnetic resonance is moving from depicting the structural consequences of disease to imaging active pathobiology, while photon-counting computed tomography and image-derived digital twins support one-stop structural assessment and in-silico prediction of therapy response. The convergence of AI, molecular imaging and advanced precision is transforming HF imaging from better pictures into smarter, integrated, personalised data that directly inform care. Realising this promise will require rigorous validation, attention to algorithmic bias and generalisability, demonstrated cost-effectiveness, curricular reform, and equitable access. This section aims to critically appraise these innovations and their translation into practice.
M. Hundertmark· Current Heart Failure Report...· 0 citations
Pulmonary hypertension (PH) is a heterogeneous clinical syndrome in which similar haemodynamic abnormalities may arise from distinct vascular, cardiac, pulmonary, thromboembolic, and molecular mechanisms. This complexity limits the ability of conventional classifications and risk scores to fully capture individual disease trajectories and treatment responses. Artificial intelligence (AI) offers a framework for integrating clinical data, electrocardiography, multimodal imaging, invasive haemodynamics, biomarkers, and multi-omics information across the PH care pathway. This review summarises current applications of machine learning and deep learning in early detection, diagnostic referral, right-ventricular and pulmonary vascular phenotyping, molecular endotyping, risk stratification, and therapeutic decision support. Available studies show promising results for AI-assisted electrocardiographic screening, automated echocardiographic and cardiac magnetic resonance analysis, computed tomography (CT)-based phenotyping, and multimodal prognostic modelling. True multi-omics integration in PH remains limited to discovery studies and has not yet yielded externally validated endotype or treatment-response classifiers. Evidence maturity is task-dependent: screening and phenotyping span several PH groups, whereas validated risk tools, molecular endotyping, and pathway-directed therapy remain predominantly PAH-based, particularly in idiopathic/heritable PAH. However, most evidence remains retrospective, derives from selected referral populations, and lacks robust external or prospective validation. No AI-based model currently supports routine drug selection or autonomous clinical decision-making. Future progress will require harmonised multicentre datasets and standardised acquisition protocols, transparent and interpretable models, and prospective studies demonstrating meaningful clinical benefit. AI should therefore be viewed as an emerging decision-support tool that may strengthen precision medicine in PH while complementing clinical expertise across diagnosis, phenotyping, risk assessment, and therapeutic stratification pathways.
Sergio Ferrantelli, Alessandro Del Cuore, G. Cassataro et al.· International Journal of Mol...· 0 citations
A deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture, which has the potential to improve cardiovascular disease prognosis and early detection, thereby increasing the scalability of sophisticated diagnostic capabilities in a variety of healthcare settings.
Taha Tahseen, Afshan Fatima· International Journal of Eng...· 0 citations
The role of AI-enhanced cardiovascular ultrasound in the transition from descriptive imaging toward predictive and personalized medicine is examined, with AI-enhanced cardiovascular ultrasound poised to become a central tool of precision cardiology.
Ancuța Elena Țupu, Simona Steliana Tudor, C. Dumitru et al.· Journal of Clinical Medicine· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
