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Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases. [ABSTRACT FROM AUTHOR]

There is currently no clear consensus on screening techniques to evaluate the presence or severity of Fontan-associated liver disease (FALD). Cardiac MRI (CMR) is used routinely for post-Fontan surveillance, but CMR-derived measures that relate to the severity of FALD are not yet defined. This was a cross-sectional single-center study of post-Fontan patients who underwent a CMR. CMR exams were re-analyzed by a single pediatric cardiologist. Surrogates of FALD included Gamma-Glutamyl Transferase (GGT), Fibrosis-4 laboratory score (FIB-4), and imaging findings. Findings consistent with cirrhosis on liver ultrasound included increased liver echogenicity and/or nodularity. Statistical analyses were performed to investigate potential relationships between CMR parameters and markers of FALD. Sixty-one patients were included. A larger inferior vena cava cross-sectional area (IVC-CSA) indexed to height was significantly associated with a higher FIB-4 score (Spearman's ρ = 0.28, p = 0.04), a higher GGT level (Spearman's ρ = 0.40, p = 0.02), and findings consistent with cirrhosis on liver ultrasound (OR 1.17, 95% CI: (1.01, 1.35), p = 0.04). None of the other CMR parameters were associated with markers of FALD. A larger indexed IVC-CSA was associated with higher systemic ventricle end-diastolic pressure (EDP) on cardiac catheterization (Spearman's ρ = 0.39, p = 0.018) as well as older age (Spearman's ρ = 0.46, p = < 0.001). Indexed IVC-CSA was the only CMR parameter that was associated with markers of FALD. This measure has the potential to serve as an additional non-invasive tool to improve screening strategies for FALD. Visual abstract summarizing the primary findings of this paper [ABSTRACT FROM AUTHOR]

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Left atrial appendage (LAA) is the source of thrombi formation in more than 90% of strokes in patients with nonvalvular atrial fibrillation. Catheter-based LAA occlusion is being increasingly applied as a treatment strategy to prevent stroke. Anatomical complexity of LAA makes percutaneous occlusion commonly performed under transesophageal echocardiography (TEE) and X-ray (XR) guidance especially challenging. Image fusion techniques integrating 3D anatomical models derived from pre-procedural imaging into the live XR fluoroscopy can be applied to guide each step of the LAA closure. Cardiac magnetic resonance (CMR) imaging gains in importance for radiation-free evaluation of cardiac morphology as alternative to gold-standard TEE or computed tomography angiography (CTA). Manual delineation of cardiac structures from non-contrast enhanced CMR is, however, labor-intensive, tedious, and challenging due to the rather low contrast. Additionally, arrhythmia often impairs the image quality in ECG synchronized acquisitions causing blurring and motion artifacts. Thus, for cardiac segmentation in arrhythmic patients, there is a strong need for an automated image segmentation method. Deep learning-based methods have shown great promise in medical image analysis achieving superior performance in various imaging modalities and different clinical applications. Fully-convolutional neural networks (CNNs), especially U-Net, have become the method of choice for cardiac segmentation. In this paper, we propose an approach for automatic segmentation of cardiac structures from non-contrast enhanced CMR images of arrhythmic patients based on CNNs implemented in a multi-stage pipeline. Two-stage implementation allows subdividing the task into localization of the relevant cardiac structures and segmentation of these structures from the cropped sub-regions obtained from previous step leading to efficient and effective way of automated cardiac segmentation. • Deep learning approaches utilizing CNNs successfully achieved state-of-the-art for cardiac image segmentation. • Proposed two-stage CNN-based approach subdivides the segmentation task into localization and subsequent segmentation. • Introduced approach yields efficient method of cardiac image segmentation in arrhythmic patients with improved performance. [ABSTRACT FROM AUTHOR]

Simple Summary: This is a contemporary overview of how cardiology issues arise in the breast cancer population from anti-cancer agents (e.g., chemotherapy, targeted therapy, endocrine therapy, and radiotherapy). The overview demonstrates how we identify these cardiac issues and what we can do about them. Breast cancer is amongst the most common invasive cancers in adults. There are established relationships between anti-cancer treatments for breast cancer and cardiovascular side effects. In recent years, novel anti-cancer treatments have been established, as well as the availability of multi-modal cardiac imaging and the sophistication of treatment for cardiac disease. This review provides an in-depth overview regarding the interface of breast cancer and cancer therapy-related cardiovascular toxicity. Specifically, it reviews the pathophysiology of breast cancer, the method of action in therapy-related cardiovascular toxicity from anti-cancer treatment, the use of echocardiography, cardiac CT, MRI, or nuclear medicine as diagnostics, and the current evidence-based treatments available. It is intended to be an all-encompassing review for clinicians caring for patients in this situation. [ABSTRACT FROM AUTHOR]

The purpose of the study was to identify factors characterizing a decrease in LV global systolic function in patients with ST-segment elevation myocardial infarction (STEMI) after revascularization using cardiac magnetic resonance imaging (MRI)-based ischemic injury pattern and laboratory parameters sensu left ventricular global systolic function. A total of 109 STEMI patients were examined. The patients underwent contrast-enhanced cardiac MRI with a 1.5 Tesla GE SIGNA Voyager (GE HealthCare, Chicago, IL, USA) on the 7th–10th days from the onset of the disease. According to cardiac MRI analysis, the patients were divided into the following groups with regard to left ventricular ejection fraction (LVEF) values: Group 1—patients with LVEF ≥ 50%; group 2—patients with mildly reduced LVEF 40–49%; group 3—patients with low LVEF < 40%. A predominance of most parameters of the ischemic injury pattern was noted in patients with mildly reduced and low LVEF versus patient group with LVEF ≥ 50%. Some risk factors for a decrease in LVEF < 50% systolic function in STEMI patients after revascularization were revealed: male gender; time from the onset of the anginal attack to revascularization; coronary artery status; several LV parameters; ischemic injury characteristics; natriuretic peptide and troponin I levels. [ABSTRACT FROM AUTHOR]

B 0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B 0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B 0 shimming is the most straightforward way to improve the B 0 homogeneity. However, today's standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large B 0 deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for B 0 shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the B 0 field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved B 0 shim compared to the standard method (SD( B 0 Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines. [ABSTRACT FROM AUTHOR]

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogenous multi-system syndrome with limited efficacious treatment options. The prevalence of Type 2 diabetes (T2D) continues to rise and predisposes patients to HFpEF, and HFpEF remains one of the biggest challenges in cardiovascular medicine today. Novel therapeutic targets are required to meet this important clinical need. Deep phenotyping studies including -OMIC analyses can provide important pathogenic information to aid the identification of such targets. The aims of this study were to determine; 1) the impact of a low-energy diet on plasma sphingolipid/ceramide profiles in people with T2D compared to healthy controls and, 2) if the change in sphingolipid/ceramide profile is associated with reverse cardiovascular remodelling. Methods: Post-hoc analysis of a randomised controlled trial (NCT02590822) including adults with T2D with no cardiovascular disease who completed a 12-week low-energy (∼810 kcal/day) meal-replacement plan (MRP) and matched healthy controls (HC). Echocardiography, cardiac MRI and a fasting blood for lipidomics were undertaken pre/post-intervention. Candidate biomarkers were identified from case–control comparison (fold change > 1.5 and statistical significance p < 0.05) and their response to the MRP reported. Association between change in biomarkers and change indices of cardiac remodelling were explored. Results: Twenty-four people with T2D (15 males, age 51.1 ± 5.7 years), and 25 HC (15 male, 48.3 ± 6.6 years) were included. Subjects with T2D had increased left ventricular (LV) mass:volume ratio (0.84 ± 0.13 vs. 0.70 ± 0.08, p < 0.001), increased systolic function but impaired diastolic function compared to HC. Twelve long-chain polyunsaturated sphingolipids, including four ceramides, were downregulated in subjects with T2D at baseline. Three sphingomyelin species and all ceramides were inversely associated with LV mass:volume. There was a significant increase in all species and shift towards HC following the MRP, however, none of these changes were associated with reverse cardiac remodelling. Conclusion: The lack of association between change in sphingolipids/ceramides and reverse cardiac remodelling following the MRP casts doubt on a causative role of sphingolipids/ceramides in the progression of heart failure in T2D. Trial registration: NCT02590822. Highlights: • This study sheds light on the emerging link between dysregulated sphingolipid/ceramide metabolism and the development of heart failure (HF) in individuals with type 2 diabetes (T2D). • Leveraging the gold standard assessment tool, cardiac MRI, this study is one of the first to explore plasma sphingolipid/ceramide in relation to measures of cardiovascular structure and function. • The findings reveal that working-aged adults with T2D and Stage A/B HF exhibit disrupted fatty acid metabolism, characterized by reduced levels of long-chain polyunsaturated sphingomyelin/ceramide species. • Importantly, this study challenges the notion of a causative role of sphingolipids/ceramides in the progression of heart failure in T2D. [ABSTRACT FROM AUTHOR]

Abstract Background Primary cardiac myxofibrosarcoma is a rare and aggressive malignancy, with the majority of approaching strategies relying on case reports. This article provides insights into its diagnosis and treatment. Case Presentation This paper presents the case of a 40‐year‐old man with sudden onset hemoptysis, leading to the diagnosis of primary cardiac myxofibrosarcoma. Treatment involved open‐heart surgery to excise the left atrium tumor, followed by 6 cycles of adjuvant chemotherapy. Unfortunately, brain metastasis developed, leading to the patient's death 1 year after initial diagnosis. Conclusion Primary cardiac myxofibrosarcoma remains a clinical challenge with an unfavorable prognosis. Early diagnosis through advanced imaging is crucial, and research is needed to explore innovative treatments. This case underscores the complexities of managing this rare cardiac malignancy and highlights the necessity for ongoing investigations to enhance patient outcomes.