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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]

The left ventricle dysfunction is the root cause for major cardiac arrests across globe. Heart functional indices such as End diastolic volume, systolic volume and Ejection Fraction are usually computed from manual segmentation of left ventricle wall (LV) in short axis MR images. This is very time consuming and suffers inter-intra observer variation due to change in shape, size and contrast of LV wall in a cardiac cycle. In this paper, deformable models are improved as compared to existing approaches with respect to their adaptation on the number of iterations per image by automatic stopping of curve evolution. This has reduced the time consumption for segmentation. The proposed strategy is based on the integrated approach of machine learning and adaptive deformable flow model with no user interaction for LV initialization. CNN is used for LV detection and shape inference which makes the approach fully automatic. The proposed methodology has improved the existing parametric deformable models in terms of the time consumption and accuracy. The proposed integrated framework has significantly improved the segmentation of LV wall for better clinical inference. For assessing the accuracy of segmentation between proposed method and ground truth, validation metrics are calculated such as percentage of good contours, Dice Metric, APD, Conformity coefficient, Pearson's Coefficient for EDV and ESV. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (MRI) segmentation task refers to the accurate segmentation of ventricle and myocardium, which is a prerequisite for evaluating the soundness of cardiac function. With the development of deep learning in medical imaging, more and more heart segmentation methods based on deep learning have been proposed. Due to the fuzzy boundary and uneven intensity distribution of cardiac MRI, some existing methods do not make full use of multi-scale characteristic information and have the problem of ambiguity between classes. In this paper, we propose a dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation. The network uses feature fusion module to preserve boundary information, and adopts the direction field module to obtain the feature maps to improve the original segmentation features. Firstly, multi-scale feature information is obtained and fused through dilated convolutional layers of different scales while downsampling. Secondly, in the decoding stage, the edge fusion block integrates the edge features into the side output of the encoder and concatenates them with the upsampled features. Finally, the concatenated features utilize the direction field to improve the original segmentation features and generate the final result. Our propose method conducts comprehensive comparative experiments on the automated cardiac diagnosis challenge (ACDC) and myocardial pathological segmentation (MyoPS) datasets. The results show that the proposed cardiac MRI segmentation method has better performance compared to other existing methods. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network's ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset. [ABSTRACT FROM AUTHOR]

Purpose of the Review: Many novel cardiac magnetic resonance imaging (cMR) techniques have been developed for diagnosis, risk stratification, and monitoring of myocardial disease. The field is changing rapidly with advances in imaging technology. The purpose of this review is to give an update on next-generation cMR techniques with promising developments for clinical translation in the last two years, and to outline clinical applications. Recent Findings: There has been increasing widespread clinical adoption of T1/T2 mapping into standard of care clinical practice. Development of auto segmentation has enabled clinical integration, with potential applications to minimize the use of contrast. Advances in diffusion tensor imaging, multiparametric mapping with cardiac MRI fingerprinting, automated quantitative perfusion mapping, metabolic imaging, elastography, and 4D flow are advancing the ability of cMR to provide further quantitative characterization to enable deep myocardial disease phenotyping. Together these advanced imaging features further augment the ability of cMR to contribute to novel disease characterization and may provide an important platform for personalized medicine. Summary: Next-generation cMR techniques provide unique quantitative imaging features that can enable the identification of imaging biomarkers that may further refine disease classification and risk prediction. However, widespread clinical application continues to be limited by ground truth validation, reproducibility of the techniques across vendor platforms, increased scan time, and lack of widespread availability of advanced cardiac MRI physicists and expert readers. However, these techniques show great promise in minimizing the need for invasive testing, may elucidate novel pathophysiology, and may provide the ability for more accurate diagnosis of myocardial disease. [ABSTRACT FROM AUTHOR]

Background: The usefulness of monitoring treatment effect of tafamidis using magnetic resonance imaging (MRI) extracellular volume fraction (ECV) has been reported. Objective: we conducted a meta-analysis to evaluate the usefulness of this method. Methods: Data from 246 ATTR-CMs from six studies were extracted and included in the analysis. An inverse variance meta-analysis using a random effects model was performed to evaluate the change in MRI-ECV before and after tafamidis treatment. The analysis was also performed by classifying the patients into ATTR-CM types (wild-type or hereditary). Results: ECV change before and after tafamidis treatment was 0.33% (95% CI: −1.83–2.49, I2 = 0%, p = 0.76 for heterogeneity) in the treatment group and 4.23% (95% CI: 0.44–8.02, I2 = 0%, p = 0.18 for heterogeneity) in the non-treatment group. The change in ECV before and after treatment was not significant in the treated group (p = 0.76), but there was a significant increase in the non-treated group (p = 0.03). There was no difference in the change in ECV between wild-type (95% CI: −2.65–3.40) and hereditary-type (95% CI: −9.28–4.28) (p = 0.45). Conclusions: The results of this meta-analysis suggest that MRI-ECV measurement is a useful imaging method for noninvasively evaluating the efficacy of tafamidis treatment for ATTR-CM. [ABSTRACT FROM AUTHOR]

Background and objectiveIn today's society, people's work pressure, coupled with irregular diet, lack of exercise and other bad lifestyle, resulting in frequent cardiovascular diseases. Medical imaging has made great progress in modern society, among which the role of MRI in cardiovascular field is self-evident. Based on this research background, how to process cardiac MRI quickly and accurately by computer has been extensively discussed. By comparing and analyzing several traditional image segmentation and deep learning image segmentation, this paper proposes the left and right atria segmentation algorithm of cardiac MRI based on UU-NET network.MethodsIn this paper, an atrial segmentation algorithm for cardiac MRI images in UU-NET network is proposed. Firstly, U-shaped upper and lower sampling modules are constructed by using residual theory, which are used as encoders and decoders of the model. Then, the modules are interconnected to form multiple paths from input to output to increase the information transmission capacity of the model.ResultsThe segmentation method based on UU-NET network has achieved good results proposed in this paper, compared with the current mainstream image segmentation algorithm results have been improved to a certain extent. Through the analysis of the experimental results, the image segmentation algorithm based on UU-NET network on the data set, its performance in the verification set and online set is higher than other grid models. The DSC in the verification set is 96.7%, and the DSC in the online set is 96.7%, which is nearly one percentage point higher than the deconvolution neural network model. The hausdorff distance (HD) is 1.2 mm. Compared with other deep learning models, it is significantly improved (about 3 mm error is reduced), and the time is 0.4 min.ConclusionThe segmentation algorithm based on UU-NET improves the segmentation accuracy obviously compared with other segmentation models. Our technique will be able to help diagnose and treat cardiac complications.

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]

This paper represents a novel level set framework for segmentation of cardiac left ventricle (LV) and right ventricle (RV) from magnetic resonance images based on anatomical structures of the heart. We first propose a level set approach to recover the endocardium and epicardium of LV by using a bi-layer level set (BILLS) formulation, in which the endocardium and epicardium are represented by the 0-level set and k -level set of a level set function. Furthermore, the recovery of LV endocardium and epicardium is achieved by a level set evolution process, called convexity preserving bi-layer level set (CP-BILLS). During the CP-BILLS evolution, the 0-level set and k -level set simultaneously evolve and move toward the true endocardium and epicardium under the guidance of image information and the impact of the convexity preserving mechanism as well. To eliminate the manual selection of the k -level, we develop an algorithm for automatic selection of an optimal k -level. As a result, the obtained endocardial and epicardial contours are convex and consistent with the anatomy of cardiac ventricles. For segmentation of the whole ventricle, we extend this method to the segmentation of RV and myocardium of both left and right ventricles by using a convex shape decomposition (CSD) structure of cardiac ventricles based on anatomical knowledge. Experimental results demonstrate promising performance of our method. Compared with some traditional methods, our method exhibits superior performance in terms of segmentation accuracy and algorithm stability. Our method is comparable with the state-of-the-art deep learning-based method in terms of segmentation accuracy and algorithm stability, but our method has no need for training and the manual segmentation of the training data. [ABSTRACT FROM AUTHOR]

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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]

Introduction: The aim of the present study was to evaluate the prevalence and prognosis of structural heart disease (SHD) among competitive athletes with negative T waves without pathological findings at transthoracic echocardiogram. Methods: From a prospective register of 450 athletes consecutively evaluated during a second-level cardiological examination, we retrospectively identified all subjects with the following inclusion criteria: (1) not previously known cardiovascular disease; (2) negative T waves in leads other than V1–V2; (3) normal transthoracic echocardiogram. Patients underwent cardiac MRI and CT. The primary endpoint was the diagnosis of definite SHD after multimodality imaging evaluation. A follow-up was collected for a combined end-point of sudden death, resuscitated sudden cardiac death and hospitalization for any cardiovascular causes. Results: A total of 55 competitive athletes were finally enrolled (50 males, 90%) with a mean age of 27.5 ± 14.1 years. Among the population enrolled 16 (29.1%) athletes had a final diagnosis of SHD. At multivariate analysis, only deep negative T waves remained statistically significant [OR (95% CI) 7.81 (1.24–49.08), p = 0.0285]. Contemporary identification of deep negative T waves and complex arrhythmias in the same patients appeared to have an incremental diagnostic value. No events were collected at 49.3 ± 12.3 months of follow-up. Conclusions: In a cohort of athletes with negative T waves at ECG, cardiac MRI (and selected use of cardiac CT) enabled the identification of 16 (29.1%) subjects with SHD despite normal transthoracic echocardiography. Deep negative T waves and complex ventricular arrhythmias were the only clinical characteristic associated with SHD diagnosis. [ABSTRACT FROM AUTHOR]