Search Results (850)

Objective: Myocardial infarction (MI) triggers inflammation and fibrosis that drive the progressive impairment of cardiac function. Yet most pharmacological studies still depend on single-time-point histological or imaging endpoints and lack longitudinal, non-invasive assessments of treatment response. Electrocardiography (ECG) detects conduction and repolarization abnormalities tightly associated with myocardial injury and structural remodeling. However, ECG monitoring in mice is limited by rigid or invasive hardware, which restricts its use for longitudinal assessment of cardiac structure and function. Approach: Here, we propose an ECG-based non-invasive post-MI cardiac remodeling assessment approach and develop a flexible electrocardiographic monitoring microsystem (FECMS). Using the anti-remodeling drug (colchicine) therapy in an MI mouse model (Sham n = 4, MI n = 7 survivors, Col n = 7 survivors) for validation, we longitudinally track drug-induced changes in ECG parameters and systematically evaluate their concordance with functional, structural, and molecular indicators of cardiac injury and remodeling. Results: Colchicine treatment induced progressive shortening of the QRS and QT intervals and gradual stabilization of the PR interval. These interval changes were accompanied by increased EF and FS, decreased LVESV, reduced myocardial fibrosis and inflammatory infiltration, and lower plasma troponin I levels at the endpoint. Correlation analyses revealed strong relationships between drug-induced changes in ECG parameters and functional recovery and inhibited structural remodeling. Significance: The FECMS provides a new, non-invasive tool for longitudinal cardiovascular drug evaluation. This approach has the potential to complement or reduce reliance on terminal histological endpoints and to facilitate the optimization of dosing strategies in preclinical cardiovascular pharmacology. Full article

Background/Objectives: Persistent and long-standing persistent atrial fibrillation (AF) presents a therapeutic clinical challenge balancing complex rhythm management with a heightened stroke risk. The left atrial appendage (LAA) is the primary source of thromboembolisms in these patients. This study evaluated the safety and efficacy of combining LAA exclusion with Convergent Hybrid Ablation for stroke prevention and rhythm control in a refractory patient cohort. Methods: A single-center observational cohort study was conducted including 28 patients with symptomatic persistent or long-standing persistent AF. The cohort was highly refractory, with 82.1% having failed at least one endocardial catheter ablation. The hybrid procedure consisted of sub-xiphoid epicardial ablation, thoracoscopic LAA exclusion (AtriClip), and endocardial catheter ablation. Safety and efficacy were assessed at 3 months and 12 months. Results: LAA exclusion was successfully performed in 96.4% of patients. The peri-operative safety profile was acceptable, with zero procedure-related strokes or deaths. At the 12-month follow-up, the rate of stroke or any other major adverse events was at 0.0%. Freedom from AF was 75.0%, shown by a 12-lead electrocardiography (ECG). Freedom from any atrial arrhythmia off anti-arrhythmic drugs (AADs) was achieved in 50.0% of patients. A total of 32.1% of the cohort required catheter ablation within 12 months to maintain sinus rhythm as part of the hybrid treatment. Conclusions: Concomitant LAA exclusion during Convergent Hybrid Ablation is a safe procedure with a high clinical success rate in maintaining sinus rhythm in a highly complex AF patient group. While no thromboembolic events were observed at 12 months, larger studies with longer follow-up are needed to confirm the potential for long-term stroke risk reduction. The findings suggest that for many patients, the hybrid procedure should be viewed as part of a multi-step strategy often requiring endocardial “touch-up” ablation. Full article

Ventricular repolarization abnormalities are among the most frequent electrocardiographic findings in pediatric athletes undergoing cardiovascular screening, yet their clinical significance remains a major source of diagnostic uncertainty. While most of them represent benign expressions of training-induced cardiac remodeling and developmental maturation, selected patterns may constitute the earliest phenotypic manifestation of cardiomyopathies or primary electrical disease. Distinguishing physiological adaptation from early pathology is therefore essential to prevent both sudden cardiac events and unnecessary restrictions on sports participation. This review integrates contemporary international electrocardiographic interpretation criteria with emerging pediatric evidence to provide a clinically oriented framework for evaluation and risk stratification of ventricular repolarization abnormalities in pediatric athletes. Early repolarization and anterior T-wave inversion are commonly benign when occurring within recognized age- and ethnicity-specific patterns and in the absence of symptoms, concerning family history, or structural abnormalities. Conversely, lateral or inferolateral T-wave inversion, atypical ST-segment morphology, complex ventricular arrhythmias, and abnormal imaging findings represent red flags requiring comprehensive investigation, including multimodality imaging when indicated. Due to the dynamic electrophysiological evolution during adolescence, longitudinal reassessment is crucial. A structured, risk-based approach integrating electrocardiographic features, demographic/familial context, clinical evaluation, imaging findings, and follow-up provides a pragmatic strategy to optimize risk detection while safeguarding appropriate athletic participation in young athletes. Full article

Previous studies on healthy controls suggest the added value of thigh-based Electrocardiography (ECG), which collects data using sensors embedded in a toilet seat for unobtrusive signal acquisition. However, further evidence regarding its clinical feasibility is needed; with this work, we investigated three complementary aspects: signal quality, morphological correlation with standard ECG leads, and the system’s potential for heart rate variability (HRV) analysis in patients undergoing aortic valve replacement. This work was divided into two main phases. In the first, 32 healthy volunteers underwent simultaneous ECG recordings using both a standard 12-lead ECG system and the thigh-based system. Signal Quality Index (SQI) analysis revealed that 56.25% of the experimental signals were classified as excellent, and over 62.5% of recordings showed a strong correlation with Lead I of the clinical ECG. These findings extend the state of the art by further characterising the quality and relevance of the captured signals. In the second phase, two patients with severe aortic stenosis were monitored before and after surgical valve replacement. HRV metrics derived from the thigh-based ECG captured distinct autonomic responses: one patient showed significant postoperative improvement in global and parasympathetic modulation (increased SDNN, RMSSD, and Sample Entropy), while the other exhibited reduced variability and complexity, potentially indicating impaired autonomic recovery. These results highlight the feasibility of thigh-based ECG data acquisition for passive, longitudinal cardiac health monitoring in everyday environments and its applicability for pre- and postoperative autonomic assessment. Full article

Acute coronary syndromes (ACS) remain time-critical clinical emergencies in which early diagnosis and accurate risk stratification determine management and outcomes. Although symptoms, electrocardiography, and high-sensitivity cardiac troponin (hs-cTn) provide a reliable framework for detecting myocardial injury, they offer limited insight into plaque instability, thromboinflammatory activity, vascular repair, and post-infarction remodeling. In this narrative review, we examine the biological rationale and current clinical evidence supporting brain-derived neurotrophic factor (BDNF) as a candidate biomarker in ACS, with particular attention to pre-analytical, analytical, and phenotypic sources of heterogeneity. Available studies show that circulating BDNF concentrations vary substantially according to biological matrix, timing of sampling, ACS subtype, and assay methodology, which likely contributes to inconsistent findings across cohorts. Overall, current evidence does not support BDNF as a diagnostic alternative to hs-cTn in rule-in or rule-out pathways. However, BDNF may have value in biological phenotyping and risk stratification by reflecting platelet activation, endothelial dysfunction, inflammatory signaling, and remodeling processes after ACS. Further progress will require standardized pre-analytical procedures, separate assessment of mature BDNF and proBDNF, serial sampling, and validation in large multicenter studies. Full article

Distributed denial-of-service (DDoS) attacks pose a critical threat to the availability of the Internet of Medical Things (IoMT). This paper proposes an intrusion detection system (IDS) based on a hybrid neuro-fuzzy-inspired approach to identify DDoS attacks in IoMT environments. The architecture combines an ensemble of decision trees, a sigmoidal smoothing mechanism, and a multilayer neural meta-classifier, enabling the modeling of nonlinear relationships between legitimate and malicious traffic without requiring explicit fuzzy rules or a formal fuzzy inference mechanism. The evaluation was conducted using the public DoS/DDoS-MQTT-IoT dataset, which was extended by incorporating legitimate traffic generated by electrocardiography (ECG) monitoring devices to approximate real operational IoMT conditions. The model was validated using stratified cross-validation and bootstrap procedures. In the extended IoMT scenario including ECG traffic, the proposed approach achieved an area under the ROC curve (AUC) of 0.904 and an F1 score of 0.823. Finally, the IDS was integrated into an intrusion detection and prevention system (IDPS) capable of detecting anomalous traffic patterns within three seconds and automatically blocking malicious IP addresses after repeated detections. 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/Objectives: The relationship between P-wave dispersion (Pd) and disease status in patients with Familial Mediterranean Fever (FMF) undergoing colchicine treatment is unclear in the literature, and results are contradictory. This study aimed to evaluate P-wave dispersion in patients with Familial Mediterranean Fever receiving regular long-term colchicine treatment and to compare these findings with those of age- and sex-matched individuals without FMF. Methods: A cross-sectional and observational study included 97 individuals with positive FMF and 97 individuals with negative FMF. FMF diagnosis was confirmed according to the Tel-HaShomer criteria, and all patients received regular colchicine treatment and were evaluated during the attack-free period. P maximum, P minimum, and Pd were measured using standard 12-lead electrocardiography (ECG); clinical, laboratory, and drug data were recorded. Pd associations were analyzed using correlation and multivariable regression. Results: Pd was found to be significantly higher in FMF (+) patients (47 vs. 39 ms, p < 0.001). Pd showed a positive correlation with FMF status (r = 0.508, p < 0.001), colchicine dose (r = 0.476, p < 0.001), white blood cell (WBC) (r = 0.209, p = 0.005) and high-density lipoprotein cholesterol (HDL-C) (r = 0.156, p = 0.037) and a negative correlation with calcium channel blocker use (r = −0.245, p = 0.001). In multivariate analysis, FMF increased Pd by 10.17 ms, while calcium channel blockers decreased it by 11.78 ms (p < 0.001). Age, WBC and HDL-C also had independent positive effects on Pd (p < 0.001, p = 0.017, p = 0.040, respectively). Conclusions: These findings suggest that FMF is associated with increased P-wave dispersion despite regular colchicine treatment, indicating persistent subclinical atrial conduction heterogeneity. Full article

The global spread of COVID-19 led to the rapid authorization of remdesivir as the first antiviral therapy. However, accumulating clinical evidence has linked its use to cardiac adverse effects. Understanding the mechanisms underlying remdesivir-induced cardiotoxicity is critical for optimizing its clinical use and ensuring patient safety. This study investigates the electrophysiological and molecular features underlying remdesivir-induced cardiac toxicity in male and female guinea pigs, aiming to elucidate the sex-dependent differences in cardiac dysfunction and the role of mitochondria in mediating these effects. A cardiac injury model was established via intraperitoneal administration of remdesivir. In vivo telemetry and ex vivo electrocardiography were used for continuous monitoring of cardiac electrical activity, while optical mapping enabled the assessment of action potential parameters and conduction properties. The histopathological alterations and mitochondrial ultrastructure were examined by hematoxylin–eosin staining and transmission electron microscopy. ELISA and Western blot analyses were performed to explore the inflammatory signaling, apoptosis, and mitochondrial dynamics. Remdesivir induced distinct sex-specific patterns of cardiac toxicity. Compared with female guinea pigs, male guinea pigs had significantly more severe myocardial injury, which was characterized by extensive inflammatory cell infiltration, marked mitochondrial disruption, and a higher incidence of sustained ventricular tachyarrhythmia. Overall, remdesivir was associated with sex-dependent cardiac toxicity, accompanied by mitochondrial impairment and inflammatory activation. Male guinea pigs were more susceptible to electrophysiological instability and mitochondrial dysfunction. These findings highlight the importance of carefully evaluating remdesivir’s cardiac effects and support the need for individualized, sex-specific considerations in its clinical administration. Full article

Smartwatches are widely used to monitor physiological responses during exercise; however, their accuracy in measuring heart rate (HR) and energy expenditure (EE) across different exercise modalities remains insufficiently characterized. This study evaluated the accuracy of HR and EE measurements obtained from four commercially available smartwatches in comparison with gold-standard reference methods. Sixty-two healthy adult men performed standardized endurance and resistance exercise protocols while simultaneously wearing four smartwatches (Apple, Galaxy, Fitbit, and Garmin). HR was measured using electrocardiography (ECG), and EE was determined using indirect calorimetry. Measurement accuracy was assessed using repeated-measures analysis of variance, Pearson’s correlation analysis, intraclass correlation coefficients (ICCs), and Bland–Altman analyses. All smartwatches demonstrated high accuracy in HR measurements during both endurance and resistance exercises. During endurance exercise, HR measurements from all smartwatch brands were comparable to those obtained via ECG, whereas during resistance exercise, only the Apple Watch showed no significant difference from the ECG. HRs showed strong correlations with ECG readings (r = 0.64–0.97), excellent reliability (ICC > 0.94), and narrow limits of agreement (approximately ±10 bpm). In contrast, the EE measurements exhibited limited accuracy across all devices. During endurance exercise, EE was consistently underestimated with wide limits of agreement. EE accuracy further deteriorated during resistance exercise, showing weak correlations with indirect calorimetry (r = 0.10–0.34) and poor reliability (ICC < 0.45). Overall, smartwatches provide accurate HR measurements across endurance and resistance exercise modalities, supporting their use in exercise intensity monitoring and HR-based training. However, smartwatch-derived EE estimates do not accurately reflect the metabolic demands, particularly during resistance exercises. Future research should focus on improving EE estimation algorithms through multimodal biosignal integration and machine-learning approaches, and validating these methods across diverse populations and exercise modalities. Full article

Cardiovascular diseases are a growing public health challenge in Ethiopia, worsened by limited access to diagnostics, including ECG, and shortages of specialized expertise. AI-powered ECG offers potential to improve diagnostic accuracy, efficiency, and access in resource-limited settings, but its adoption is influenced by policy, regulatory, financing, and governance factors, which are not well understood in Ethiopia. This study explored these system-level determinants using qualitative methods from September to October 2025 across federal institutions, four regions, and five tertiary hospitals. Twenty-five stakeholders, including policymakers, regulators, digital health experts, and hospital leaders, were interviewed. Data were transcribed verbatim, coded inductively, and analyzed thematically. Six themes emerged: policy and governance, regulatory frameworks, financing and cost considerations, data governance and bias, integration barriers, and sustainability recommendations. Findings showed AI-powered ECG interpretation aligns with Ethiopia’s digital health and noncommunicable disease priorities, but the country lacks AI-specific health policies, clear regulations, and dedicated budgets. Financing is largely donor-dependent, data governance and algorithmic bias remain concerns, and infrastructure gaps and digital skill shortages limit readiness. Study participants recommended learning from prior digital health projects, coordinated scale-up, phased implementation, and continuous monitoring. Effective adoption will require context-specific policies, sustainable financing, robust regulation, strong data governance, and careful system integration to ensure equitable, responsible, and sustainable use. Full article

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality globally, with low-resource settings, including Ethiopia facing challenges due to limited early diagnostic services. AI-powered electrocardiography (ECG) interpretation has the potential to improve diagnostic accuracy, decentralize care, and support timely clinical decisions, but evidence on healthcare providers’ perspectives and adoption determinants is limited. This exploratory descriptive qualitative study employed 31 in-depth interviews with healthcare providers. Healthcare providers (cardiologists, internists, cardiac and critical care nurses, critical care specialists, and general practitioners) were purposively selected through maximum variation sampling from ten hospitals in four regions of Ethiopia. Data were transcribed verbatim, coded inductively, and analyzed thematically. The data analysis identified six themes: perceived benefit of AI-powered ECG interpretation CDSS, trust development, workflow integration, ethical concerns, functionality, and adoption determinants. Participants emphasized AI’s potential to enhance accessibility, consistency, and diagnostic accuracy while reducing subjectivity and unnecessary referrals. Acceptance relied on high accuracy, reliable data, and rigorous validation, with the technology seen as supportive rather than replacing clinicians. Material resources, human resource readiness, and leadership engagement were key factors for adoption. Recommendations included phased implementation, continuous training, and model expansion to ensure sustainability and clinical utility. The AI-powered ECG interpretation CDSS was viewed as a valuable adjunct for strengthening cardiovascular care in Ethiopia, highlighting the need for context-sensitive strategies, ethical safeguards, and multi-level system readiness for successful adoption. Full article

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article

Cardiac amyloidosis (CA) remains underrecognized due to overlapping features with other cardiovascular conditions, including hypertrophic cardiomyopathy and hypertensive heart disease. Certain ‘red flag’ features across the clinical and imaging spectrum help identify CA. However, these features are often absent, subtle, or inconsistently recognized, particularly in early disease, and are atypical phenotypes. This leads to frequent delays in diagnosis and presentation at advanced stages. Artificial intelligence (AI) offers a promising approach to detect subtle disease signatures by integrating multimodal and longitudinal data beyond human pattern recognition. AI-enhanced electrocardiography has emerged as a scalable screening tool, demonstrating high diagnostic performance and enabling earlier detection. In parallel, echocardiographic AI has evolved toward video-based analysis, improving standardization and reducing inter-reader variability. Similarly, AI applications in cardiac magnetic resonance and nuclear scintigraphy allow for automated quantification and more reproducible assessment of amyloid burden. Beyond diagnosis, emerging models support disease phenotyping, risk stratification, and treatment monitoring. This review synthesizes current applications of AI across multimodal testing in the evaluation and diagnosis of CA. Full article