Search Results (357)

Monitoring cardiovascular health enables continuous and real-time risk assessment. This study utilized the Framingham Heart Study dataset to develop and evaluate machine-learning models for predicting mortality risk based on key cardiovascular parameters. Some machine-learning algorithms were applied to multiple machine-learning models. Among these, XGBoost achieved the highest predictive performance, each with an area under the curve (AUC) value of 0.83. Feature importance analysis revealed that coronary artery disease, glucose levels, and diastolic blood pressure (DIABP) were the most significant risk factors associated with mortality. The primary contribution of this research lies in its implications for public health and preventive medicine. By identifying key risk factors, it becomes possible to calculate individual and population-level risk scores and to design targeted early intervention strategies aimed at reducing cardiovascular-related mortality. Full article

Accurate segmentation of cardiac MR images using deep neural networks is crucial for cardiac disease diagnosis and treatment planning, as it provides quantitative insights into heart anatomy and function. However, achieving high segmentation accuracy relies heavily on extensive, precisely annotated datasets, which are costly and time-consuming to obtain. This study addresses this challenge by proposing a novel data augmentation framework based on a condition-guided diffusion generative model, controlled by multiple cardiac labels. The framework aims to expand annotated cardiac MR datasets and significantly improve the performance of downstream cardiac segmentation tasks. The proposed generative data augmentation framework operates in two stages. First, a Label Diffusion Module is trained to unconditionally generate realistic multi-category spatial masks (encompassing regions such as the left ventricle, interventricular septum, and right ventricle) conforming to anatomical prior probabilities derived from noise. Second, cardiac MR images are generated conditioned on these semantic masks, ensuring a precise one-to-one mapping between synthetic labels and images through the integration of a spatially-adaptive normalization (SPADE) module for structural constraint during conditional model training. The effectiveness of this augmentation strategy is demonstrated using the U-Net model for segmentation on the enhanced 2D cardiac image dataset derived from the M&M Challenge. Results indicate that the proposed method effectively increases dataset sample numbers and significantly improves cardiac segmentation accuracy, achieving a 5% to 10% higher Dice Similarity Coefficient (DSC) compared to traditional data augmentation methods. Experiments further reveal a strong correlation between image generation quality and augmentation effectiveness. This framework offers a robust solution for data scarcity in cardiac image analysis, directly benefiting clinical applications. Full article

Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of $0.82\pm 0.13$ was obtained with Method D versus $0.63\pm 0.09$ with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality. Full article

Cardiovascular diseases (CVDs) remain the leading cause of death globally, underscoring the urgent need for data-driven early diagnostic tools. This study proposes a multilayer artificial neural network (ANN) model for heart disease prediction, developed using a real-world clinical dataset comprising 13,981 patient records. Implemented on the Orange data mining platform, the ANN was trained using backpropagation and validated through 10-fold cross-validation. Dimensionality reduction via principal component analysis (PCA) enhanced computational efficiency, while Shapley additive explanations (SHAP) were used to interpret model outputs. Despite achieving 83.4% accuracy and high specificity, the model exhibited poor sensitivity to disease cases, identifying only 76 of 2233 positive samples, with a Matthews correlation coefficient (MCC) of 0.058. Comparative benchmarks showed that random forest and support vector machines significantly outperformed the ANN in terms of discrimination (AUC up to 91.6%). SHAP analysis revealed serum creatinine, diabetes, and hemoglobin levels to be the dominant predictors. To address the current study’s limitations, future work will explore LIME, Grad-CAM, and ensemble techniques like XGBoost to improve interpretability and balance. This research emphasizes the importance of explainability, data representativeness, and robust evaluation in the development of clinically reliable AI tools for heart disease detection. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Background and Objectives: Coronavirus Disease 2019 (COVID-19) may cause extensive multi-organ pathology, particularly in the lungs, heart, kidneys, and liver. While hypercoagulability—often signaled by elevated D-dimer—has been thoroughly investigated, the concurrent pathological findings across organs and their interrelation with distinct D-dimer levels remain incompletely characterized. This study aimed to evaluate the pathological changes observed in autopsied or deceased COVID-19 patients, focusing on the prevalence of organ-specific lesions, and to perform subgroup analyses based on three D-dimer categories. Methods: We conducted a retrospective review of 69 COVID-19 patients from a Romanian-language dataset, translating all clinical and pathological descriptions into English. Pathological findings (pulmonary microthrombi, bronchopneumonia, myocardial fibrosis, hepatic steatosis, and renal tubular necrosis) were cataloged. Patients were grouped into three categories by admission D-dimer: <500 ng/mL, 500–2000 ng/mL, and ≥2000 ng/mL. Laboratory parameters (C-reactive protein, fibrinogen, and erythrocyte sedimentation rate) and clinical outcomes (intensive care unit [ICU] admission, mechanical ventilation, and mortality) were also recorded. Intergroup comparisons were performed with chi-square tests for categorical data and one-way ANOVA or the Kruskal–Wallis test for continuous data. Results: Marked organ pathology was significantly more frequent in the highest D-dimer group (≥2000 ng/mL). Pulmonary microthrombi and bronchopneumonia increased stepwise across ascending D-dimer strata (p < 0.05). Myocardial and renal lesions similarly showed higher prevalence in patients with elevated D-dimer. Correlation analysis revealed that severe lung and heart pathologies were strongly associated with high inflammatory markers and a greater risk of ICU admission and mortality. Conclusions: Our findings underscore that COVID-19-related organ damage is magnified in patients with significantly elevated D-dimer. By integrating pathology reports with clinical and laboratory data, we highlight the prognostic role of hypercoagulability and systemic inflammation in the pathogenesis of multi-organ complications. Stratifying patients by D-dimer may inform more tailored management strategies, particularly in those at highest risk of severe pathology and adverse clinical outcomes. Full article

Purpose: Despite increasing rates of prostate cancer among men, prostate cancer risk assessments continue to rely on invasive laboratory tests like prostate-specific antigen and Gleason score tests. This study aimed to develop a noninvasive, data-driven risk model for patients to evaluate themselves before deciding whether to visit a hospital. Materials and Methods: To train the model, data from the National Health Insurance Sharing Service cohort datasets, comprising 347,575 individuals, including 1928 with malignant neoplasms of the prostate, 5 with malignant neoplasms of the penis, 18 with malignant neoplasms of the testis, and 14 with malignant neoplasms of the epididymis, were used. The risk model harnessed easily accessible inputs, such as history of treatment for diseases including stroke, heart disease, and cancer; height; weight; exercise days per week; and duration of smoking. An additional 286,727 public datasets were obtained from the National Health Insurance Sharing Service, which included 434 (0.15%) prostate cancer incidences. Results: The risk calculator was built based on Cox proportional hazards regression, and I validated the model by calibration using predictions and observations. The concordance index was 0.573. Additional calibration of the risk calculator was performed to ensure confidence in accuracy verification. Ultimately, the actual proof showed a sensitivity of 60 (60.5) for identifying a high-risk population. Conclusions: The feasibility of the model to evaluate prostate cancer risk without invasive tests was demonstrated using a public dataset. As a tool for individuals to use before hospital visits, this model could improve public health and reduce social expenses for medical treatment. Full article

Background: Clostridioides difficile infection (CDI) is a major cause of healthcare-associated morbidity and mortality. Understanding the predictors of in-hospital mortality and recurrence of CDI is key for improving outcomes. This study combined traditional statistical methods and machine learning approaches to identify risk factors for these outcomes. Methods: We conducted a nationwide, retrospective study using the Spanish Minimum Basic Data Set at Hospitalization, analyzing 34,557 admissions with CDI from 2020 to 2022. Logistic regression combined with the least absolute shrinkage and selection operator (LASSO) was used to identify the most relevant predictors. Survival analyses using Cox regression and LASSO were also performed to assess time-to-mortality predictors. Results: Mortality and recurrence rates increased over the study period, reflecting the increasing burden of CDI. LASSO identified a parsimonious subset of predictors while maintaining predictive accuracy (area under the curve: 0.71). Older age (OR = 2.10, 95%CI: 1.98–2.22), Charlson Comorbidity Index ≥ 2 (OR = 1.42, 95%CI: 1.33–1.52), admission to the intensive care unit (OR = 3.09, 95%CI: 2.88–3.32), congestive heart failure (OR = 1.71, 95%CI: 1.61–1.82), malignancies (OR = 1.76, 95%CI: 1.66–1.87), and dementia (OR = 1.36, 95%CI: 1.25–1.48) were strongly associated with all-cause hospital mortality. For recurrence, age ≥ 75 years (OR = 1.19, 95%CI: 1.12–1.27), chronic kidney disease (OR = 1.15, 95%CI: 1.08–1.23), and chronic liver disease (OR = 1.43, 95%CI: 1.16–1.74) were the strongest predictors, while malignancy appeared protective, likely due to survivor bias. Conclusions: Our study provides a robust framework for predicting CDI outcomes. The integration of traditional statistical methods and machine learning applied to a large dataset may improve the reliability of the results. Our findings highlight the need for targeted interventions in high-risk populations and emphasize the potential utility of machine learning in risk stratification. Future studies should validate these models in external cohorts and explore survivor bias in malignancy-associated outcomes. Full article

Cardiovascular diseases remain one of the leading causes of mortality worldwide, highlighting the importance of effective monitoring and early diagnosis. While electrocardiography (ECG) is the standard technique for evaluating the heart’s electrical activity and detecting rhythm and conduction abnormalities, it alone is insufficient for identifying certain conditions, such as valvular disorders. Phonocardiography (PCG) allows the recording and analysis of heart sounds and improves the diagnostic accuracy when combined with ECG. In this study, ECG and PCG signals were simultaneously acquired from a resting adult subject using a compact system comprising an analog front-end (model AD8232, manufactured by Analog Devices, Wilmington, MA, USA) for ECG acquisition and a digital stethoscope built around a condenser electret microphone (model HM-9250, manufactured by HMYL, Anqing, China). Both the ECG electrodes and the microphone were positioned on the chest to ensure the spatial alignment of the signals. An adaptive segmentation algorithm was developed to segment PCG and ECG signals based on their morphological and temporal features. This algorithm identifies the onset and peaks of S1 and S2 heart sounds in the PCG and the Q, R, and S waves in the ECG, enabling the extraction of the systolic time intervals such as EMAT, PEP, LVET, and LVST parameters proven useful in the diagnosis and monitoring of cardiovascular diseases. Based on the segmented signals, the measured averages (EMAT = 74.35 ms, PEP = 89.00 ms, LVET = 244.39 ms, LVST = 258.60 ms) were consistent with the reference standards, demonstrating the reliability of the developed method. The proposed algorithm was validated on synchronized ECG and PCG signals from multiple subjects in an open-source dataset (BSSLAB Localized ECG Data). The systolic intervals extracted using the proposed method closely matched the literature values, confirming the robustness across different recording conditions; in detail, the mean Q–S1 interval was 40.45 ms (≈45 ms reference value, mean difference: −4.85 ms, LoA: −3.42 ms and −6.09 ms) and the R–S1 interval was 14.09 ms (≈15 ms reference value, mean difference: −1.2 ms, LoA: −0.55 ms and −1.85 ms). In conclusion, the results demonstrate the potential of the joint ECG and PCG analysis to improve the long-term monitoring of cardiovascular diseases. Full article

The separation of respiratory and cardiac sounds is a significant challenge in biomedical signal processing due to their overlapping frequency and time characteristics. Traditional methods struggle with accurate extraction in noisy or diverse clinical environments. This study explores the application of machine learning, particularly convolutional neural networks (CNNs), to overcome these obstacles. Advanced machine learning models, denoising algorithms, and domain adaptation strategies address challenges such as frequency overlap, external noise, and limited labeled datasets. This study presents a robust methodology for detecting heart and lung diseases from audio signals using advanced preprocessing, feature extraction, and deep learning models. The approach integrates adaptive filtering and bandpass filtering as denoising techniques and variational autoencoders (VAEs) for feature extraction. The extracted features are input into a CNN, which classifies audio signals into different heart and lung conditions. The results highlight the potential of this combined approach for early and accurate disease detection, contributing to the development of reliable diagnostic tools for healthcare. Full article

Background: Chronic diseases significantly burden healthcare systems due to the need for long-term treatment. Early diagnosis is critical for effective management and minimizing risk. The current traditional diagnostic approaches face various challenges regarding efficiency and cost. Digitized healthcare demonstrates several opportunities for reducing human errors, increasing clinical outcomes, tracing data, etc. Artificial Intelligence (AI) has emerged as a transformative tool in healthcare. Subsequently, the evolution of Generative AI represents a new wave. Large Language Models (LLMs), such as ChatGPT, are promising tools for enhancing diagnostic processes, but their potential in this domain remains underexplored. Methods: This study represents the first systematic evaluation of ChatGPT’s performance in chronic disease prediction, specifically targeting heart disease and diabetes. This study compares the effectiveness of zero-shot, few-shot, and CoT reasoning with feature selection techniques and prompt formulations in disease prediction tasks. The two latest versions of GPT4 (GPT-4o and GPT-4o-mini) are tested. Then, the results are evaluated against the best models from the literature. Results: The results indicate that GPT-4o significantly beat GPT-4o-mini in all scenarios regarding accuracy, precision, and F1-score. Moreover, a 5-shot learning strategy demonstrates superior performance to zero-shot, few-shot (3-shot and 10-shot), and various CoT reasoning strategies. The 5-shot learning strategy with GPT-4o achieved an accuracy of 77.07% in diabetes prediction using the Pima Indian Diabetes Dataset, 75.85% using the Frankfurt Hospital Diabetes Dataset, and 83.65% in heart disease prediction. Subsequently, refining prompt formulations resulted in notable improvements, particularly for the heart dataset (5% performance increase using GPT-4o), emphasizing the importance of prompt engineering. Conclusions: Even though ChatGPT does not outperform traditional machine learning and deep learning models, the findings highlight its potential as a complementary tool in disease prediction. Additionally, this work provides value by setting a clear performance baseline for future work on these tasks Full article

Background/Objectives: Accurate and efficient diagnosis of heart disease through electrocardiogram (ECG) analysis remains a critical challenge in clinical practice due to noise interference, morphological variability, and the complexity of overlapping cardiac signals. Methods: This study presents a comprehensive deep learning (DL) framework that integrates advanced ECG signal segmentation with transfer learning-based classification, aimed at improving diagnostic performance. The proposed ECG segmentation algorithm introduces a distinct and original approach compared to prior research by integrating adaptive preprocessing, histogram-based lead separation, and robust point-tracking techniques into a unified framework. While most earlier studies have addressed ECG image processing using basic filtering, fixed-region cropping, or template matching, our method uniquely focuses on automated and precise reconstruction of individual ECG leads from noisy and overlapping multi-lead images—a challenge often overlooked in previous work. This innovative segmentation strategy significantly enhances signal clarity and enables the extraction of richer and more localized features, boosting the performance of DL classifiers. The dataset utilized in this work of 12 lead-based standard ECG images consists of four primary classes. Results: Experiments conducted using various DL models—such as VGG16, VGG19, ResNet50, InceptionNetV2, and GoogleNet—reveal that segmentation notably enhances model performance in terms of recall, precision, and F1 score. The hybrid VGG19 + SVM model achieved 98.01% and 100% accuracy in multi-class classification, along with average accuracies of 99% and 97.95% in binary classification tasks using the original and reconstructed datasets, respectively. Conclusions: The results highlight the superiority of deep, feature-rich models in handling reconstructed ECG signals and confirm the value of segmentation as a critical preprocessing step. These findings underscore the importance of effective ECG segmentation in DL applications for automated heart disease diagnosis, offering a more reliable and accurate solution. Full article

The relevance of O₃ exposure in critical congenital heart disease (CCHD) remains uncertain and requires further investigation. The present study aims at quantitatively assessing the association between ambient O₃ exposure during the early pregnancy period with fetal CCHD and identifying possible susceptible exposure windows. A retrospective cohort study involving 24,516 pregnant women was conducted using data from the Maternal–Fetal Medicine Consultation Network, which encompassed 1313 medical centers across China from 2013 to 2021. We extracted daily O₃ concentrations from a validated grid dataset with a spatial resolution of 0.1° at each participant’s residential county to assess ambient O₃ exposure, followed by calculating the average exposure levels in the periconceptional period, embryonic period, first trimester, and preconception period. The diagnosis of CCHD was based on fetal echocardiography. Exposure–response analyses were carried out using logistic regression models. During the study period, a total of 1541 (17.4%) subjects were diagnosed with fetal CCHD. Each 10 µg/m³ increase in ambient O₃ exposure in the periconceptional period was associated with a 26.0% increase in the odds of CCHD (odds ratio [OR]: 1.260, 95% confidence interval [CI]: 1.189, 1.335; p < 0.001). Importantly, the association was not modified by factors including maternal age and occupation status, paternal age and smoking status, conception mode, and the presence of risk factors. In the sensitivity analysis, significant associations were observed between O₃ exposure and CCHD in the embryonic period, first trimester, and preconception period, which was consistent with the results of the main analyses. These findings suggest that lowering ambient O₃ exposure in the preconception and early pregnancy periods may be beneficial in reducing the risk of fetal CCHD, especially in regions with elevated O₃ levels. Full article

The early diagnosis of dementia, a progressive condition impairing memory, cognition, and functional ability in older adults, is essential for timely intervention and improved patient outcomes. This study proposes a novel multiclass classification that differentiates dementia from other comorbid conditions, specifically cardiovascular diseases, including heart failure and aortic valve disorder, by leveraging the “blessing of dimensionality” to enhance predictive performance while ensuring feature accessibility. Using a dataset of 26,474 electronic health records from two hospitals in Chiang Rai, Thailand, the proposed framework introduced clinically informed feature augmentation to enhance model generalizability. Furthermore, the borderline synthetic minority oversampling technique was employed to address class imbalance, enhancing the model’s performance for minority classes. This study systematically evaluated a suite of machine learning models, including extreme gradient boosting, gradient boosting, random forest, support vector machine, decision trees, k-nearest neighbors, extra trees, and TabNet, across both the original and enriched datasets, with the latter integrating augmented features and synthetic data. Predictive performance was assessed using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and area under the precision–recall curve. The results revealed that all the models exhibited consistent performance improvements with the enriched dataset, affirming the value of dimensionality when guided by domain expertise. Full article

Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a systemic disorder, often coexisting with chronic obstructive pulmonary disease (COPD). This study aims to identify the shared pathogenic mechanisms between HFpEF and COPD and validate them in an experimental HFpEF model. Transcriptomic datasets from HFpEF cardiac tissue and COPD lung tissue were analyzed using differentially expressed gene (DEG) analysis, weighted gene co-expression network analysis (WGCNA), and functional enrichment analysis. Key genes were identified through least absolute shrinkage and selection operator (LASSO) regression. Immune cell infiltration was assessed using xCell and CIBERSORT, and single-cell RNA sequencing (scRNA-seq) was utilized to determine gene expression patterns across different cell populations. A high-fat diet and N[w]-nitro-L-arginine methyl ester (L-NAME)-induced HFpEF mouse model was established, and the expression of SESN3 and autophagy-related markers was evaluated in both cardiac and pulmonary tissues using immunofluorescence, quantitative PCR (qPCR), Western blotting (WB), and transmission electron microscopy. DEG and WGCNA analyses identified 1243 and 131 core genes in HFpEF and COPD, respectively. Functional enrichment analysis highlighted autophagy as a common regulatory pathway in both conditions. Among the nine intersecting genes, SESN3 was identified as a key candidate through LASSO regression. Immune infiltration analysis and scRNA-seq further demonstrated the involvement of SESN3 in both cardiac and pulmonary pathophysiology. In vivo experiments showed that HFpEF mice exhibited significant lung injury. Furthermore, SESN3 upregulation and autophagy dysregulation were observed in both heart and lung tissues, supporting a potential systemic role of SESN3-mediated autophagy in HFpEF-related pulmonary alterations. This study suggests that SESN3-mediated autophagy may represent a shared mechanism between HFpEF and COPD. Our findings suggest that HFpEF may be associated with pulmonary alterations beyond cardiac dysfunction alone. These results provide novel insights into the potential multi-organ involvement in HFpEF and support the role of SESN3 as a shared molecular target in both cardiac and pulmonary pathologies. Full article