The assessment of left atrial strain (LAS) has emerged as an essential component in the evaluation of cardiac function, especially in pathologies such as heart failure and atrial fibrillation. This narrative review aims to outline the available methods for assessing LAS with a major emphasis on speckle-tracking echocardiography techniques. Other imaging modalities, including cardiac magnetic resonance and cardiac computed tomography, also provide important information on LA dynamics but have disadvantages with respect to cost and availability. The current narrative review underlines basic concepts such as the accurate assessment of LAS and discusses the clinical relevance of LAS by pointing out its significant diagnostic and prognostic role in several cardiovascular conditions. The aim of this article is to discuss the current integration of LAS into clinical practice with a view to further improving patient management and treatment strategies.

Immune checkpoint inhibitor (ICI) therapy has dramatically improved the prognosis for some cancers but can be associated with myocarditis, adverse cardiovascular events, and mortality. The aim of this study was to develop an artificial intelligence (AI) model to predict the increased likelihood for the development of ICI-related myocarditis and adverse cardiovascular events. Cancer patients treated with ICI at a tertiary institution from 2011 to 2022 were reviewed. Baseline characteristics, laboratory values, electrocardiograms, and cardiovascular clinical outcomes were extracted. A composite outcome of ICI-related myocarditis and major adverse cardiovascular events (transient ischemic attack/stroke, new diagnosis of heart failure, myocardial infarction, and cardiac death) was used to develop a multimodal joint fusion AI model by combining baseline tabular data with electrocardiogram (ECG) in a single end-to-end model. ECG data were parsed using 1-D convolution and tubular data using multilayer perceptron. Of 2,258 cancer patients who had ICI therapy and troponin measurement (mean age 68.5 ± 11.5 years, 59.7% male), the composite of cardiovascular clinical adverse events, including ICI-related myocarditis and major adverse cardiovascular events, occurred in 264 (11.7%) unique patients, with 428 events overall (including 59 [3%] ICI-related myocarditis events and 59 [3%] cardiac deaths). The proposed joint fusion model outperformed individual ECG and baseline electronic medical record data and laboratory value models with an area under the operating characteristics curve of 0.72 (0.64 true positive rate and 0.98 negative predictive value). A multimodal fusion AI model to predict myocarditis and adverse cardiovascular events in cancer patients starting ICI therapy had good prognostic performance. It may have clinical utility in identifying at-risk patients who may benefit from closer surveillance.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy. It follows an autosomal dominant inheritance pattern in most cases, with incomplete penetrance and heterogeneity. It is familial in 60% of cases and most of these are caused by pathogenic variants in the core sarcomeric genes (MYH7, MYBPC3, TNNT2, TNNI3, MYL2, MYL3, TPM1, ACTC1). Genetic testing using targeted disease-specific panels that utilize next-generation sequencing (NGS) and include sarcomeric genes with the strongest evidence of association and syndrome-associated genes is highly recommended for every HCM patient to confirm the diagnosis, identify the molecular etiology, and guide screening and management. The yield of genetic testing for a disease-causing variant is 30% in sporadic cases and up to 60% in familial cases and in younger patients with typical asymmetrical septal hypertrophy. Genetic testing remains challenging in the interpretation of results and classification of variants. Therefore, in 2015 the American College of Medical Genetics and Genomics (ACMG) established guidelines to classify and interpret the variants with an emphasis on the necessity of periodic reassessment of variant classification as genetic knowledge rapidly expands. The current guidelines recommend focused cascade genetic testing regardless of age in phenotype-negative first-degree relatives if a variant with decisive evidence of pathogenicity has been identified in the proband. Genetic test results in family members guide longitudinal clinical surveillance. At present, there is emerging evidence for genetic test application in risk stratification and management but its implementation into clinical practice needs further study. Promising fields such as gene therapy and implementation of artificial intelligence in the diagnosis of HCM are emerging and paving the way for more effective screening and management, but many challenges and obstacles need to be overcome before establishing the practical implications of these new methods.

Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. Conclusions: While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.

Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.

Athlete’s heart (AH) represents the heart’s remarkable ability to adapt structurally and functionally to prolonged and intensive athletic training. Characterized by increased left ventricular (LV) wall thickness, enlarged cardiac chambers, and augmented cardiac mass, AH typically maintains or enhances systolic and diastolic functions. Despite the positive health implications, these adaptations can obscure the difference between benign physiological changes and early manifestations of cardiac pathologies such as dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic cardiomyopathy (ACM). This article reviews the imaging characteristics of AH across various modalities, emphasizing echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography as primary tools for evaluating cardiac function and distinguishing physiological adaptations from pathological conditions. The findings highlight the need for precise diagnostic criteria and advanced imaging techniques to ensure accurate differentiation, preventing misdiagnosis and its associated risks, such as sudden cardiac death (SCD). Understanding these adaptations and employing the appropriate imaging methods are crucial for athletes’ effective management and health optimization.