Citations

The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient’s unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy.

Abstract Arrhythmogenic cardiomyopathy (ACM) is addressed in international guidelines and expert consensus statements. To assist clinicians in their routine practice and support decision-making, we performed a systematic review of the most relevant guidelines and recommendations for ACM diagnosis and management. Our search, covering MEDLINE, EMBASE, and resources from scientific societies over the last 10 years, identified two guidelines and three consensus statements that met rigorous inclusion criteria for detailed analysis. In the examined documents, key areas of agreement included the critical role of cardiac imaging for initial diagnosis and ongoing monitoring, genetic testing in index patients, ventricular arrhythmia management, catheter ablation indications, heart failure treatment strategies, and exercise recommendations. However, significant differences were found in definitions and diagnostic criteria for ACM, interpretation of phenocopies, management of family members, and criteria for implantable cardioverter defibrillator implantation. Additional discrepancies emerged regarding the role of multidisciplinary teams, non-cardiac surgical considerations, atrial fibrillation management, and reproductive issues. Crucially, there remain considerable gaps in evidence, especially in areas such as the management and follow-up of patients with potential or borderline ACM diagnoses, as well as the care of their relatives. The clinical implications of genetic findings, along with the clinical management of left-dominant, biventricular phenotypes, and hot phases of disease, are also insufficiently addressed. Furthermore, a critical shortfall is the lack of externally validated risk assessment tools to guide clinical decision-making. Bridging these gaps could help guiding future research and guideline development towards improving patient outcomes.

Abstract Dilated cardiomyopathy (DCM) is a clinical diagnosis characterized by the presence of left ventricular dilatation and systolic dysfunction unexplained by abnormal loading conditions or coronary artery disease. However, a broad range of phenotypic manifestations, encompassing isolated scar, DCM with preserved ejection fraction, and overt DCM, should be regarded as a diagnostic classification representing a broad spectrum of underlying aetiologies, including both inherited and acquired heart muscle disorders. A multimodal non-invasive imaging approach is essential for accurate morpho-functional assessment of cardiac chambers and is key to establish the cardiac phenotype and to rule out an underlying ischaemic aetiology. Furthermore, advanced imaging techniques enable deep cardiovascular phenotyping and non-invasive tissue characterization. The aim of this review is to propose a systematic approach to the diagnosis of DCM, emphasizing the importance of genetics and clinical findings for a precise and practical clinical approach. Also, we strive to qualify the role of cardiac imaging in the diagnosis of DCM, particularly on the relevance of novel techniques and clinical utility of actionable parameters to improve current diagnostic schemes and risk stratification algorithms. We further elaborate on the role of cardiac imaging to deliver optimal guidance to aetiology-based therapeutic approaches, verification of treatment response and disease progression monitoring.

Abstract Dilated cardiomyopathy (DCM) is extensively discussed in numerous expert consensus documents and international guidelines, with differing recommendations. To support clinicians in daily practice and decision-making, we conducted a systematic review of key guidelines and recommendations concerning the diagnosis and clinical management of DCM. Our research encompassed MEDLINE and EMBASE databases for relevant articles published, as well as the websites of relevant scientific societies. We identified two guidelines and one scientific statement that met stringent criteria, thereby qualifying them for detailed systematic analysis. Our review revealed consensus on several key aspects: the definition of DCM, the use of B-type natriuretic peptides and high-sensitivity troponin in laboratory testing, the essential role of multimodality cardiovascular imaging for initial diagnosis, genetic counselling, and the management of advanced disease. Nonetheless, notable areas of variation included the formation of multidisciplinary management teams, the role of cascade genetic testing, pathways for arrhythmic risk stratification, and the criteria for prophylactic defibrillator implantation. Significant evidence gaps persist, particularly regarding the clinical trajectory of genetic, non-genetic and gene-elusive forms of DCM, the use of cardiovascular magnetic resonance in phenotype-negative family members with genotype-positive probands, and the development of potential aetiology-oriented therapies. Addressing these gaps could enhance clinical outcomes and inform future research directions and guideline development.

Background: Mitral valve prolapse (MVP) is a common valvular abnormality. Observational studies demonstrate an association between MVP and increased risk of sudden cardiac death from ventricular arrhythmias, coined arrhythmogenic MVP. A genetic component has been postulated but remains under-investigated. Hypothesis: Predicted deleterious variants in Mendelian cardiomyopathy and arrhythmia genes are more frequent in arrhythmogenic MVP (aMVP) compared to non-arrhythmogenic MVP. Aims: To comprehensively phenotype and genotype a single-center aMVP cohort and compare to non-aMVP from two large population-based cohorts and investigate their clinical outcomes. Methods: The aMVP cohort included 46 prospectively enrolled patients from a regional referral center for inherited cardiovascular disease and had undergone multi-modality imaging and clinical gene panel testing. The non-aMVP comparator cohorts included 453 participants from the UK Biobank (UKBB), a prospective cohort of 500,000 UK residents, and 1,418 participants from the National Institutes of Health (NIH) All of Us (AoU), a prospective cohort of 450,000 USA participants. Gene variants were annotated in ANNOVAR. Participants with and without pathogenic/likely pathogenic (P/LP) variants reported on ClinVar with ≥2-star level of evidence were considered genotype positive (G+) and negative (G-) respectively. Results: The demographics for aMVP (age 61.2 ± 12.8 years, 56.5% female) and non-aMVP (UKBB: age 59.8 ± 7.3 years, 50.7% female and AoU: age 65.3 ± 13.4 years, 76.2% female) were comparable. G+ participants were more prevalent in the aMVP cohort 10/46 (21.7%) versus non-aMVP in the UKBB 8/453 (1.8%) and in the AoU 21/1418 (1.5%); p<0.001. The most frequently implicated gene in aMVP was TTN . Of 46 in aMVP cohort, 33 (71%) underwent CMR. G+ participants had a higher frequency of late gadolinium enhancement [6/6 (100%) vs. 14/27 (51.8%), p<0.001] and wall motion abnormalities [3/6 (50%) vs. 1/27 (3.7%), p<0.001] compared to G- participants. The aMVP cohort had significantly more implantable cardioverter defibrillator (ICD) implants compared to non-aMVP cohort [5/46 (10.9%) vs. 1/453 in UKBB (0.2%), p<0.001]. G+ aMVP cohort showed a trend for developing heart failure and ICD implants compared to G- aMVP . Conclusion: P/LP variants in cardiomyopathy and arrhythmia genes are more prevalent in aMVP participants compared to non-aMVP participants. This could be a hitherto unrecognized effect modifier predicting aMVP .