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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]

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]