Search Results (846)

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

Background: R wave amplitude in the aVL ECG lead (R_aVL) has been identified as a marker of cardiovascular (CV) risk, hypertension-mediated target organ damage (HMOD), and mortality in patients with arterial hypertension (AH), where R_aVL > 1.1 mV suggests left ventricular hypertrophy. However, the exact threshold for identifying high-risk patients has yet to be determined. Therefore, we compared R_aVL values among hypertensive patients with and without hypertensive urgencies (HUs) and healthy subjects, aiming to identify the predictors of elevated R_aVL and to compare its prognostic value with the SCORE 2 model. Methods: This cross-sectional study included 339 participants divided into three groups according to ambulatory blood pressure monitoring results: 100 patients with AH and HU from the emergency department, 134 patients with AH without HU, and 105 healthy subjects recruited from four family medicine practices. Basic laboratory parameters were determined, SCORE 2 risk was calculated, PWV was measured using oscillometry, and a standard 12-lead ECG was recorded in all participants. Results: Participants with AH and HU had the highest R_aVL values compared to those with AH without HU and healthy subjects (averages of 0.76 ± 0.24 mV, 0.49 ± 0.27, 0.22 ± 0.25, respectively; p < 0.001). Significantly higher R_aVL values were observed in males compared to females (0.56 ± 0.31 vs. 0.41 ± 0.34 mV; p < 0.001) and in non-dippers compared to dippers (0.56 ± 0.34 mV vs. 0.41 ± 0.31 in dippers; p < 0.001). Age, mean arterial pressure, PWV, and SCORE 2 were shown as independent predictors of R_aVL. Compared with SCORE 2, individuals with R_aVL > 0.40 mV had high CV risk (sensitivity of 58.16%, specificity of 73.68%; p < 0.001). Conclusions: In this study, R_aVL demonstrated good prognostic value for CV risk stratification. However, larger studies are needed to determine a precise high-risk threshold to improve CV risk estimation and HMOD detection in patients with marginal SCORE 2 CV risk. Full article

Background/Objectives: Cardiovascular complications remain the leading cause of mortality among patients with end-stage renal disease (ESRD) treated with maintenance hemodialysis (HD). Global longitudinal strain (GLS) is a sensitive echocardiographic marker of left ventricular systolic dysfunction that enables the detection of transient contractile abnormalities consistent with intradialytic myocardial stunning. This study aimed to assess intradialytic GLS dynamics during a single HD session and to identify predictors of GLS deterioration. Methods: Forty-three patients were enrolled. Transthoracic echocardiography, electrocardiography, and pulse wave analysis were performed before HD, at mid-session, and after HD. Biochemical assessment included, among others, plasma osmolality, electrolytes, and biomarkers of oxidative stress and endothelial dysfunction. Results: Three distinct intradialytic GLS trajectories were identified: GLS worsening (GLSw, 46.5%), GLS stable (GLSs, 34.9%), and GLS improvement (GLSi, 18.6%). In the GLSw group, independent predictors of GLS deterioration included a decrease in left atrial volume index (LAVI, p = 0.0002), an increase in left ventricular end-systolic volume index (LVESVI, p = 0.0067), diabetes mellitus (p = 0.0094), and an increase in the malondialdehyde-to-creatinine ratio (MDA/CREA, p = 0.0055). In the GLSi group, GLS improvement was associated with a decrease in plasma osmolality (p = 0.0326) and asymmetric dimethylarginine (ADMA, p = 0.0279), as well as an increase in the subendocardial viability ratio index (SEVRI, p = 0.0004) and caspase-1 (p = 0.0005). Conclusions: Intradialytic GLS trajectories are heterogeneous and reflect individual susceptibility to GLS deterioration. Modifiable adverse factors likely include oxidative stress, osmotic stress, fluid overload, uremic toxin- and ion-disturbance-related stress, and impaired coronary microvascular reserve. Future prospective studies are needed. Full article

Tricuspid regurgitation (TR) is a prevalent cardiovascular disorder with significant clinical impact. TR is frequently silent and underdiagnosed and is estimated to impact over 70 million people globally. Characterized by retrograde blood flow from the right ventricle into the right atrium due to incomplete valve closure, TR leads to right heart dilation, systemic congestion, and eventually right-sided heart failure. Importantly, TR may contribute to the onset of atrial fibrillation (AF), the most common sustained arrhythmia, affecting approximately 59 million individuals worldwide. Despite its growing clinical importance, the pathophysiology of TR remains incompletely understood, and current animal models of TR, based on direct valve manipulation, limit translational applicability. We present a novel, minimally invasive porcine model of TR established via femoral/jugular vein catheterization with deployment of an inferior vena cava (IVC) filter. The filter partially impedes tricuspid valve closure, inducing TR without valvular injury. Validation was achieved through multimodal imaging, including fluoroscopy, echocardiography, and electrocardiography, confirming hallmark features of TR, including right atrial and ventricular enlargement and arrhythmic activity. This model provides a reproducible, minimally invasive platform for studying selected features of TR progression. Its minimally invasive nature and preservation of native valvular structure make it a useful preclinical platform for mechanistic and translational research. Full article

Advances in implantable and wearable cardiac monitoring technologies have led to widespread detection of brief, often asymptomatic atrial high-rate episodes, frequently labelled as device-detected atrial fibrillation (AF). While detection has increased substantially, the clinical interpretation of these findings remains uncertain. Observational studies demonstrate associations between AF burden and stroke risk but reveal marked inter-individual heterogeneity and no consistent temporal threshold below which risk is eliminated. Recent randomised controlled trials show that anticoagulation guided solely by arrhythmia duration confers limited net clinical benefit, with modest reductions in ischaemic stroke offset by increased bleeding. These findings challenge the biological and clinical validity of rigid time-based thresholds for intervention. Increasing evidence suggests that AF may act primarily as a marker of underlying atrial disease rather than the sole mechanistic cause of thromboembolism. This article provides an evidence-informed perspective on the interpretation of device-detected AF in contemporary clinical practice and argues for a shift away from duration-based triggers toward a longitudinal, risk-adapted approach that integrates AF trajectory, atrial substrate, and clinical context. Emerging tools such as artificial intelligence-enhanced electrocardiography may help identify occult atrial pathology but must augment rather than replace clinical judgement. Proportionate, individualised care should supersede reflexive treatment strategies in the management of device-detected AF. Full article