Search Results (690)

Brugada syndrome (BrS) is a rare but potentially life-threatening condition that may lead to sudden cardiac death. Among the causes, dysfunctions of ion channels involved in the cardiac action potential (specifically in SCN5A and SCN10A genes) are particularly significant. Among diagnosed Brugada patients, fever-induced episodes occur in 20–30% of cases. Fever worsens sodium channel dysfunction, as elevated temperature further reduces their conductance. First clinical manifestation of BrS occurs usually during a febrile episode, especially in young people. We performed a multiparametric examination in addition to genetic analysis. We treated a 19-year-old man presenting with subfebrility. During the patient’s subfebrile episodes, 12-lead ECG recordings revealed ST-segment elevations in leads V1–V3. Notably, the patient remained asymptomatic. Targeted genetic testing of SCN5A did not reveal any disease-causing variants as an underlying cause of the syndrome, but the temperature-inducing effect was demonstrated. The occurrence of the Brugada type 1 pattern has also been observed at subfebrile episodes, although significantly rarely. This case demonstrates that in susceptible patients, even a relatively mild elevation in body temperature can trigger ion channel dysfunctions. Timely diagnosis and follow-up are important in preserving quality of life and preventing fatal outcomes. Full article

Medical image segmentation plays a crucial role in clinical diagnosis and treatment planning. However, existing segmentation frameworks frequently exhibit high computational complexity and often fail to retain fine-grained structural details—especially along intricate anatomical boundaries such as blood vessels and tumor margins. To overcome these limitations, we propose VMMedSAM-X, an efficient and computationally economical medical image segmentation framework that incorporates structured state space modeling into the Medical Segment Anything Model (MedSAM) architecture. The proposed method adopts a state-enhanced encoder that combines extended long short-term memory (xLSTM) with two-dimensional selective scanning (SS2D) and a dual-path cross-attention mechanism to enhance long-range dependency modeling while maintaining linear computational complexity. Experiments conducted on the $1024\times 1024$ ACDC cardiac MRI dataset show that the proposed encoder reduces floating-point operations from 369.44 G to 17.36 G and achieves a $2.4\times$ improvement in inference speed compared with the Vision Transformer (ViT)-based encoder. Additional evaluations on the SegTHOR and MSD-Lung datasets demonstrate consistent improvements in Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics over MedSAM and Vision Mamba U-Net (VM-UNet) baselines. These results indicate that the proposed framework provides an effective and computationally efficient solution for high-resolution medical image segmentation. Full article

Background: Despite advances in reperfusion therapy, ST-segment elevation myocardial infarction (STEMI) remains associated with substantial morbidity and mortality. Early identification of predictors of adverse outcomes is essential for improving risk stratification. Methods: This retrospective study included 512 STEMI patients who underwent coronary revascularization within 6 h of symptom onset. Clinical, laboratory, angiographic and echocardiographic variables were analyzed. The primary endpoint was in-hospital mortality. Secondary outcomes included reduced left ventricular ejection fraction (LVEF < 40%) and moderate-to-severe ischemic mitral regurgitation (IMR). Independent predictors of in-hospital mortality were identified using multivariable logistic regression, while secondary outcomes were described to characterize the study population. Model performance was evaluated using ROC analysis. Results: In-hospital mortality occurred in 9.4% of patients. Reduced LVEF was present in 26.2%, and IMR in 10.9%. Independent predictors of mortality included LVEF < 40% (OR 5.72, 95% CI 2.77–11.80, p < 0.001), IMR (OR 2.61, 95% CI 1.14–5.97, p = 0.023), lower hemoglobin levels (OR 0.74, 95% CI 0.61–0.91, p = 0.003), and reduced glomerular filtration rate (OR 0.96, 95% CI 0.95–0.98, p < 0.001). The model demonstrated good discrimination (AUC 0.88). Complete revascularization was not independently associated with mortality. Conclusions: Left ventricular dysfunction, IMR, anemia, and renal impairment are strong predictors of in-hospital mortality in STEMI patients. Integrating echocardiographic and laboratory parameters may improve early risk stratification and guide clinical decision-making. Full article

Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly. Full article

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis. Full article

Background and Clinical Significance: Intrapericardial ectopic thyroid tissue is extremely rare and can mimic vascular mediastinal or cardiac lesions. Case Presentation: We describe a 62-year-old woman with dyspnea, palpitations, and flushing for several months, progressively worsening, associated with nonspecific ST-segment abnormalities on ECG. Contrast-enhanced CT revealed a small, highly vascularized epicardial mass anterior to the ascending aorta. 18F-FDG PET/TC findings were inconclusive, and biopsy was not feasible due to the anatomical location. Surgical excision via upper ministernotomy was performed, leading to resolution of symptoms. Histology confirmed benign ectopic thyroid tissue. Conclusions: With fewer than ten similar intrapericardial cases reported in the English-language medical literature, this presentation underlines the diagnostic difficulty of such lesions and the importance of including ectopic thyroid tissue among the less common differential diagnostic considerations for intrapericardial masses, particularly in patients with prior thyroid disease. Full article

Background/Objectives: Masters athletes are adults aged ≥40 who compete in sport, exhibiting superior physical function and healthier aging than their sedentary peers. However, even highly trained masters athletes experience age-related performance declines. Women masters athletes represent a growing yet understudied population who may face unique physiological challenges. This scoping review synthesizes literature from 1984 to 2024, examining the impact of age and menopause on determinants of endurance performance in women masters athletes. Methods: Following JBI scoping review methodology, six databases were searched (Medline, Embase, Central, CINAHL, SPORTdiscus, Scopus). Studies were evaluated for population characteristics, methodological approaches, and physiological determinants of performance (i.e., aerobic capacity, lactate kinetics, and exercise economy). Results: Twenty-nine studies were included. Most (n = 28) assessed aerobic capacity, reporting declines between 0.36 and 0.84 mL·kg⁻¹·min⁻¹·year⁻¹ (0.5–2.4%·year⁻¹). These reductions were primarily associated with decreased cardiac output followed by changes in body composition. Training volume emerged as a predictor of aerobic capacity, but the effects of menopause were unclear. Findings on lactate kinetics and exercise economy were mixed but preliminary research indicated that lactate threshold relative to VO₂max generally increased, peak lactate remained stable and energy cost increased with age. Fitness and health characteristics among women athletes differed from sedentary populations, emphasizing the need for athlete-specific data to support training and health decisions. Conclusions: Aging is associated with decreased aerobic capacity and variable changes in lactate kinetics and exercise economy. While training volume may attenuate performance decrements, the impact of menopause remains uncertain, underscoring the need for longitudinal research to better support this growing segment of the population. Full article

Background: Artificial intelligence (AI) and deep learning (DL) are rapidly changing the field of diagnostics and imaging in cardiology, offering tools for automatic segmentation, quantification of changes, and risk stratification. These technologies have the potential to increase diagnostic accuracy, work efficiency, and individualization of patient care. Methods: This structured narrative review critically evaluates clinically validated applications of artificial intelligence (AI) and deep learning (DL) in cardiovascular medicine, focusing on imaging (echocardiography, coronary CT angiography, cardiac MRI, and ECG), risk stratification, and biomarker integration. A systematic literature search was conducted in PubMed for studies published between January 2015 and December 2026, supplemented by references from key articles. Original English-language studies reporting quantitative clinical outcomes were included, with 78 studies ultimately analyzed. Results: AI and DL models, including convolutional neural networks and transformers, achieved performance comparable to experts in cardiac imaging, myocardial perfusion assessment, valve defect detection, and coronary event prediction. Multimodal approaches improved diagnostic accuracy and reproducibility, while explainable AI enhanced transparency and clinical confidence. Deep learning also enabled faster image acquisition and processing without compromising precision. Conclusions: AI and DL have transformative potential in cardiology, offering fast, accurate, and scalable diagnostic tools. The integration of multimodal data, the validation of algorithms in prospective studies, and ensuring the transparency of models are key. Future research should focus on prospective, multicenter validations and the ethical and safe implementation of AI in everyday clinical practice. Full article

Background and Objectives: Drug-coated balloons (DCBs) represent a novel, attractive strategy for coronary revascularization; however, data supporting their use in complex real-world populations remain limited. We aimed to evaluate the safety and efficacy of a DCB-first strategy in a predominantly acute coronary syndrome (ACS) and multivessel disease (MVD) population. Materials and Methods: We conducted a prospective two-center observational registry including 115 consecutive patients treated with a DCB-first strategy (DCB-only in 44 patients and a hybrid DCB–drug-eluting stent in 71 patients) for both de novo and in-stent coronary lesions. Bailout stenting was performed when required according to predefined criteria. Results: The study population was characterized by high clinical complexity, with 78.3% MVD and 67.8% presenting with ACS, including 10.5% ST-segment elevation myocardial infarctions. Bailout stenting was required in 12.2% of lesions. At 18 months, the target lesion revascularization (TLR) rate was 2.83%, while the device-oriented composite endpoint (DOCE; cardiac death, target vessel myocardial infarction or TLR) occurred in 4.7% of patients. The cumulative major adverse cardiovascular event (MACE) rate at 18 months was 14.8%, largely driven by the high-risk clinical profile of the cohort. Patients treated with a DCB-only strategy had a shorter duration of dual antiplatelet therapy compared with those treated with a hybrid strategy. Conclusions: In this two-center real-world registry including predominantly ACS and MVD patients, a DCB-first strategy was associated with low lesion-level event rates and acceptable mid-term clinical outcomes. These findings support the feasibility of a leave-nothing-behind approach in complex coronary disease when meticulous lesion preparation and provisional bailout stenting are applied. Full article

Background/Objective: Cardiac innervation plays a critical role in regulating myocardial function and enabling the heart to adapt to physiological and pathological conditions. Although the general features of sympathetic and parasympathetic innervation of the myocardium are well described, the spatial organisation of nerve fibres within the cardiac muscle remains incompletely characterised. This study aimed to develop and validate the SKUF (Slice–Keep–Unwrap–Fuse) protocol, a multimodal framework for mapping myocardial innervation through the integration of histological data and magnetic resonance imaging (MRI). Methods: The study was performed on the heart of a 7-year-old patient who died from rupture of a cerebral vascular malformation without evidence of cardiovascular disease. Prior to histological processing, post-mortem MRI was performed to provide a precise anatomical reference. The heart was sectioned into sequential transverse rings of 4 mm thickness, yielding 71 paraffin blocks. Histological sections (3 μm) were immunostained with antibodies against UCHL-1 to visualise nerve fibres and scanned using an Aperio AT2 system (20× magnification). Automated image analysis was conducted using the SVSSlide Processor module, which included tissue segmentation, colour-based nerve fibre detection, and sliding-window density mapping. Heatmaps were assembled into ring-based myocardial reconstructions and co-registered with MRI slices using combined rigid and deformable registration, followed by three-dimensional reconstruction of innervation patterns. Results: A higher density of nerve fibres was observed in the right ventricular myocardium compared with the left ventricle, whereas larger nerve trunks were identified in the epicardium of the left ventricle. Quantitative analysis revealed a pronounced longitudinal gradient of innervation, with minimal density in the apical region and progressive increases towards the mid-ventricular segments, where maximal density and spatial organisation of neural structures were observed. The atrioventricular groove exhibited the greatest heterogeneity of innervation due to the presence of large nerve trunks and ganglionated plexuses. Integration of histological maps with MRI enabled three-dimensional visualisation of spatial clusters of nerve fibres. Conclusions: The SKUF protocol provides a robust framework for integrating histological and MRI data to generate three-dimensional maps of myocardial innervation. This approach may facilitate the development of high-resolution anatomical atlases of cardiac innervation and support future studies of neurocardiac mechanisms of arrhythmogenesis and targeted neuromodulation. Full article

Precise segmentation of cardiac substructures in magnetic resonance imaging is pivotal for diagnosis and treatment planning but remains impeded by anatomical scale heterogeneity and the scarcity of high-quality pixel-level annotations. Existing deep learning paradigms often struggle to simultaneously resolve the global geometry of ventricular cavities and the fine-grained boundaries of the myocardium, particularly in low-data regimes. To address these challenges, we propose SAS-SemiUNet++, a holistic semi-supervised segmentation framework. This architecture incorporates two novel mechanisms: (1) The Scale-Aware Semantic Recalibration (SASR) unit, which functions as a dynamic semantic gate to adaptively adjust receptive fields, mimicking a radiologist’s variable-focus mechanism to capture multi-scale anatomical details, and (2) Stochastic Consistency Regularization (SCR), a dual-path perturbation strategy that enforces geometric invariance on unlabeled data, thereby mitigating overfitting to noisy pseudo-labels. Comprehensive evaluations on the ACDC benchmark demonstrate that SAS-SemiUNet++ significantly outperforms state-of-the-art methods, achieving superior segmentation accuracy and boundary fidelity, particularly in reducing the 95% Hausdorff distance. This study presents a data-efficient and robust solution for cardiac image analysis, offering potential for scalable clinical deployment. Full article

Inflammatory myocardial involvement has been reported in canine leishmaniosis (CanL); however, studies evaluating the degree of myocardial dysfunction in affected dogs are limited. This prospective study aimed to investigate myocardial involvement in dogs with CanL using conventional and speckle-tracking echocardiography (STE), focusing on the assessment of left ventricular systolic function and myocardial strain. Symptomatic, initially untreated dogs with a diagnosis of leishmaniosis and free from other vector-borne diseases or underlying heart diseases were enrolled (Leish group). Healthy dogs matched for age, body weight, breed, and sex were selected for the control group (C group). At the time of inclusion (T0) and at each follow-up, laboratory tests as well as conventional echocardiographic examination and STE were performed. For strain analysis, apical longitudinal long-axis 4-chamber, 3-chamber, and 2-chamber views were used (2C, 3C, 4C, respectively) to obtain the average global longitudinal strain (GLSAV), which is recognised to have the maximum reliability as an indicator of left ventricular dysfunction in humans. The software obtains GLSAV by averaging the longitudinal strain values from all left-ventricular segments derived from the multiple apical views. After enrolment, dogs were treated with a combination of meglumine and allopurinol and were monitored for six months. Clinical-pathological and echocardiographic data were collected at follow-up at 1, 3, and 6 months after the start of treatment (T1, T2, T3) and compared between the two study groups using appropriate statistical tests. Sixteen dogs composed the C group and nine dogs the Leish group. At T0, none of these dogs had abnormalities in cardiac auscultation, plasma cardiac troponin concentration was within the reference range, and standard echocardiographic examination excluded underlying cardiac diseases. The comparison between C and Leish groups did not show a statistically significant difference in any of the strain parameters analysed (GLSAV, GLS4C, GLS3C, GLS2C). Moreover, strain values in the Leish group did not change significantly over time. In conclusion, in this preliminary study on a limited population of dogs with leishmaniosis, both conventional echocardiography and STE failed to reveal clear changes suggestive of left ventricular systolic dysfunction secondary to possible myocarditis or as a consequence of the systemic disease in dogs with active leishmaniosis. However, further STE studies in larger cohorts of dogs with leishmaniosis are needed to confirm and expand our findings. Full article

Accurate detection of cardiac arrhythmias from electrocardiograms remains challenging for rare rhythm classes due to class imbalance and morphological variability. We present a hybrid deep learning framework combining per-lead convolutional encoders with a cross-lead transformer that models relationships across different lead signals through self-attention, accepting variable lead configurations. To address minority-class scarcity, a generative adversarial network synthesizes physiologically plausible beat segments for underrepresented arrhythmias. Attention-based visualizations localize influential waveform regions aligned with clinically meaningful structures. Post-training pruning and INT8 quantization enable efficient deployment with minimal performance loss. Extensive experiments on the MIT-BIH Arrhythmia Database across sixteen heartbeat classes from two-lead recordings yield exceptional results over ten independent runs: accuracy of 99.67%, F1-score of 99.66%, and AUC of 99.8%. External validation on the ECG5000 single-lead dataset and the St Petersburg INCART twelve-lead dataset confirms robust generalizability with F1-scores of 97.6% and 98% respectively. Our framework delivers accurate, interpretable, stable, and deployable arrhythmia detection across diverse clinical settings. Full article

Cardiovascular imaging is a cornerstone of modern cardiology, yet its clinical impact is limited by operator dependence, inter-observer variability, time-consuming workflows, and unequal access to advanced expertise. Artificial intelligence (AI), particularly machine learning and deep learning, offers new opportunities to overcome these limitations. This review aims to summarize current and emerging AI applications in cardiovascular imaging and to evaluate their potential clinical value in precision diagnostics and decision support. This narrative review synthesizes clinically relevant literature on AI applications across major cardiovascular imaging modalities, including echocardiography, cardiovascular magnetic resonance, cardiac computed tomography, and nuclear cardiology. Evidence was analyzed with a focus on AI-enabled acquisition support, image segmentation, quantitative and functional assessment, workflow automation, and risk stratification, alongside key methodological and implementation considerations. Across imaging modalities, AI-driven approaches have demonstrated improved reproducibility, efficiency, and scalability of cardiovascular imaging workflows. Automated algorithms reduce operator dependence, facilitate standardized extraction of imaging biomarkers, and support advanced functional assessment and prognostic stratification. Recent developments in video-based, temporal, and multimodal models further expand AI capabilities from technical automation toward integrated disease phenotyping and personalized clinical decision support. However, translation into routine practice remains limited by heterogeneous datasets, insufficient external validation, algorithmic bias, limited interpretability, and challenges related to regulatory approval and workflow integration. Artificial intelligence has the potential to reshape cardiovascular imaging into a more efficient, reproducible, and patient-centered precision medicine tool. Real-world clinical impact will depend on outcome-driven evaluation, robust external validation, multimodal data integration, and human-in-the-loop implementation strategies that ensure safe, equitable, and clinically meaningful adoption. Full article

Continuous blood pressure (BP) monitoring is essential for the prevention and management of cardiovascular diseases. Traditional cuff-based methods cause discomfort during repeated measurements, and wearable sensors require direct skin contact, limiting their applicability. Radar-based contactless BP measurement has emerged as a promising alternative. However, radar pulse wave (RPW) signals are susceptible to motion artifacts, respiratory interference, and environmental clutter, posing persistent challenges to estimation accuracy and robustness. In this paper, we propose MARU-MTL, a Mamba-enhanced multi-task learning framework for continuous BP estimation using a single millimeter-wave radar sensor. To address signal quality degradation, a Variational Autoencoder-based Signal Quality Index (VAE-SQI) mechanism is proposed to automatically screen RPW segments without manual annotation. To capture long-range temporal dependencies across cardiac cycles, we integrate a Bidirectional Mamba module into the bottleneck of a U-Net backbone, enabling linear-time sequence modeling with respect to the segment length. We also introduce a multi-task learning strategy that couples BP regression with arterial blood pressure waveform reconstruction to strengthen physiological consistency. Extensive experiments on two datasets comprising 55 subjects demonstrate that MARU-MTL achieves mean absolute errors of 3.87 mmHg and 2.93 mmHg for systolic and diastolic BP, respectively, meeting commonly used AAMI error thresholds and achieving metrics comparable to BHS Grade A. Full article