The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The objective of this study was to leverage data from the National Health and Nutritional Examination Survey (NHANES) to develop an interpretable predictive machine learning (ML) model for HL. In accordance with the established inclusion and exclusion criteria, a total of 2814 participants were randomly assigned to one of two distinct groups for the training and validation of the predictive models. We identified the most significant variables using Recursive Feature Elimination and constructed a HL prediction model through various ML models. The generalization ability of the models was evaluated via 10-fold cross-validation. Eight different models were utilized to develop the optimal prediction model for HL. Subsequently, three interpretable methods, Feature importance analysis, Generalized linear model (GLM) and Restricted cubic spline (RCS) were integrated into a pipeline and embedded in ML for model interpretation. In this study, the Random Forest (RF) exhibited superior performance across all evaluation metrics after balancing the data using the Synthetic Minority Oversampling Technique (SMOTE), particularly excelling in AUC, PR-AUC and F1 score. Feature importance analysis uncovered significant correlations between HL and top 10 features, including age, blood lead (Pb) level, urine thallium (Tl) level, BMI, total energy, urine antimon (Sb) level, vitamin E intake, urine cobalt (Co) level, calcium intake and urine cesium (Cs) level. Moreover, both univariate and multivariate GLMs identified blood Pb [OR (95 % CI):1.169 (1.037,1.311)] and vitamin E intake [OR (95 % CI):0.776 (0.641,0.928)] as the main features associated with HL. The RCS analysis further revealed that increased blood Pb level and decreased vitamin E intake correspond to a proportional rise in the anticipated risk of HL after adjusted by confounders. Our ML models identify key factors that, if validated by future studies, will have important implications for hearing conservation. Furthermore, these ML-based point-of-care prediction models will help overcome barriers to hearing healthcare and enable the efficient allocation of resources by accurately identifying individuals who are in dire need of hearing assessment.

Background: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electrophysiology, offering new tools to interpret complex datasets, improve diagnosis, optimize clinical workflows, and personalize therapy. ChatGPT-4o, a leading AI-based language model, exemplifies the transformative potential of AI in clinical research, medical education, and patient care. Aim and Methods: In this paper, we present an exploratory dialogue with ChatGPT to assess the role of AI in shaping the future of cardiology, with a particular focus on arrhythmia management and cardiac electrophysiology. Topics discussed include AI applications in ECG interpretation, arrhythmia detection, procedural guidance during ablation, and risk stratification for sudden cardiac death. We also examine the risks associated with AI use, including overreliance, interpretability challenges, data bias, and generalizability. Conclusions: The integration of AI into cardiovascular care offers the potential to enhance diagnostic accuracy, tailor interventions, and support decision-making. However, the adoption of AI must be carefully balanced with clinical expertise and ethical considerations. By fostering collaboration between clinicians and AI developers, it is possible to guide the development of reliable, transparent, and effective tools that will shape the future of personalized cardiology and electrophysiology.

Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart, grounded in the Hodgkin-Huxley (HH) model was established to simulate cardiac electrophysiology, and ECG signals from 200 representative points were acquired. Two types of arrhythmia characterized by significant anomalies in the variables of the HH model were simulated, and corresponding synthetic ECG signals were generated. A multi-objective optimization method based on non-dominated sorting was integrated into the crayfish optimization algorithm (MOCOA). To optimize the key parameters K and [Formula: see text] in variational mode decomposition (VMD), a MOCOA-VMD technique specifically tailored for ECG signal processing was developed. The Pareto optimal front was generated using MOCOA with the indicators of spectral kurtosis and KL divergence, by which the optimal intrinsic mode functions were obtained. A deep VMD-attention network based on MOCOA was developed for ECG signal classification. The ablation study evaluated the effectiveness of the proposed signal decomposition method and deep attention modules. The model based on MOCOA-VMD achieves the highest accuracy of 94.46%, outperforming models constructed using EEMD, VMD, CNN and LSTM modules. Bayesian optimization was employed to fine-tune the hyperparameters and further enhance the performance of the deep model, with the best accuracy of the deep attention model after TPE optimization reaching 96.11%. Moreover, the real-world MIT-BIH arrhythmia database was utilized for further validation to prove the robustness and generalizability of the proposed model. The proposed deep VMD-attention modeling and classification strategy has shown significant promise and may offer valuable inspiration for other signal processing fields as well.

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic versus data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parametrization, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardized and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical and highly controlled approach provided by in silico methods has become an integral part of physiological and medical research. In silico methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic and therapeutic approaches.This article is part of the theme issue 'Science into the next millennium: 25 years on'.

Extensive research is being conducted in fabricating flexible dry electrodes for electrocardiogram monitoring, but the electrodes' efficacy in clinical settings remains underexplored. In transition from research to commercial settings, investigating the electrode's performance in real-time monitoring and patient's comfort is very crucial. This study compares the ECG signal quality between flexible silver nanorods embedded in polydimethylsiloxane (AgNRs-PDMS) dry electrodes and commercially available metal electrodes. This study, conducted in a hospital, involves 50 subjects (40 males, 10 females; age range: 20-74) among which 41 were with cardiovascular disease and 9 normal subjects. The fabricated dry electrodes are biocompatible and have a lower skin-to-electrode impedance than the commercial electrodes, resulting in high-fidelity ECG signals. Signal quality was assessed based on parameters such as signal-to-noise ratio, mean amplitude, maximum amplitude, power spectral density, and heart rate comparison. The AgNRs-PDMS electrodes demonstrated superior SNR, confirmed using a paired t-test, with a p-value close to 0, indicating a significant difference in comparison with commercial electrodes. The amplitude of ECG signals captured by AgNRs-PDMS electrodes and the heart rate were observed to be comparable to metal electrodes. For automated arrhythmia classification of the ECG signals, two models were implemented. The first model utilized R-R interval for arrhythmic rhythm classification, while the second model used principal component analysis (PCA) for dimensionality reduction followed by support vector machine (SVM) to classify arrhythmic beats. Large arrhythmia data sets like the MIT-BIH arrhythmia database were used for training and validating the above models. Accuracy results from the MIT-BIH test data set were 97% for the R-R interval method and 93% for the SVM method. The heart beats obtained from an arrhythmic patient using commercial metal electrodes and AgNRs-PDMS electrodes were classified using the classifiers. The AgNRs-PDMS dry electrodes offer superior signal quality, ease of use due to gel-free nature, and reusability, making them a promising alternative to commercial electrodes for clinical ECG monitoring.

Artificial intelligence (AI) is everywhere, but how is this expansive technology being used in cardiovascular care? This article explores common AI models, how they are transforming healthcare delivery, and important roles for clinicians, including advanced practice providers, in the development, adoption, evaluation, and ethical use of AI in cardiovascular care.

Artificial intelligence (AI) is everywhere, but how is this expansive technology being used in cardiovascular care? This article explores common AI models, how they are transforming healthcare delivery, and important roles for clinicians, including advanced practice providers, in the development, adoption, evaluation, and ethical use of AI in cardiovascular care.

This study looked at possible targets for hypertrophic cardiomyopathy (HCM), a condition marked by thickening of the ventricular wall, primarily in the left ventricle. We employed differential gene analysis and weighted gene co-expression network analysis (WGCNA) on samples. We then carried out an enrichment analysis. We also investigated the process of immunological infiltration. We employed six machine learning techniques and two protein-protein interaction (PPI) network gene selection approaches to search for the most characteristic gene (MCG). In the validation ladder, we verified the expression of MCG. Furthermore, we examined the MCG expression levels in HCM animal and cell models. Finally, we performed molecular docking and predicted potential medications for HCM treatment. 7975 differentially expressed genes (DEGs) were found in our study. We also identified 236 genes in the blue module using WGCNA. Screening at the transcriptome and protein levels was used to mine MCG. The final result screened CCAAT/Enhancer Binding Protein Delta (CEBPD) as MCG. We confirmed that MCG expression matched the outcomes of the experimental ladder. The level of CEBPD mRNA and protein was lowered in HCM animal and cellular models. Given that Abt-751 had the highest binding affinity to CEBPD, it might be a projected targeted medication. We found a new target gene for HCM called CEBPD, which is probably going to function by mitochondrial dysfunction. An innovative aim for the management or avoidance of HCM is offered by this analysis. Abt-751 may be a predicted targeted drug for HCM that had the greatest binding affinity with CEBPD.

The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medical contexts, particularly in the treatment and monitoring of atrial fibrillation. Although previous studies in explainable AI have demonstrated potential to address this issue, they often focus solely on electrocardiography graphs and lack real-world field insights.

We addressed this gap by incorporating standardized clinical interpretation of electrocardiography graphs into the system and collaborating with cardiologists to co-design and evaluate this approach using real-world patient cases and data.

We conducted a 3-stage iterative design process with 23 cardiologists to co-design, evaluate, and pilot an explainable AI application. In the first stage, we identified 4 physician personas and 7 explainability strategies, which were reviewed in the second stage. A total of 4 strategies were deemed highly effective and feasible for pilot deployment. On the basis of these strategies, we developed a progressive web application and tested it with cardiologists in the third stage.

The final progressive web application prototype received above-average user experience evaluations and effectively motivated physicians to adopt it owing to its ease of use, reliable information, and explainable functionality. In addition, we gathered in-depth field insights from cardiologists who used the system in clinical contexts.

Our study identified effective explainability strategies, emphasized the importance of curating actionable features and setting accurate expectations, and suggested that many of these insights could apply to other disease care contexts, paving the way for future real-world clinical evaluations.

Atrial fibrillation (AF) is a prevalent arrhythmia associated with significant morbidity and mortality. Despite advancements in ablation techniques, predicting recurrence of AF remains a challenge, necessitating reliable models to identify patients at risk of relapse. Traditional scoring systems often lack applicability in diverse clinical settings and may not incorporate the latest evidence-based factors influencing AF outcomes. This study aims to develop an explainable artificial intelligence model using Bayesian networks to predict AF relapse postablation, leveraging on easily obtainable clinical variables.

This study aims to investigate the effectiveness of Bayesian networks as a predictive tool for AF relapse following a percutaneous pulmonary vein isolation (PVI) procedure. The objectives include evaluating the model's performance using various clinical predictors, assessing its adaptability to incorporate new risk factors, and determining its potential to enhance clinical decision-making in the management of AF.

This study analyzed data from 480 patients with symptomatic drug-refractory AF who underwent percutaneous PVI. To predict AF relapse following the procedure, an explainable artificial intelligence model based on Bayesian networks was developed. The model used a variable number of clinical predictors, including age, sex, smoking status, preablation AF type, left atrial volume, epicardial fat, obstructive sleep apnea, and BMI. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) metrics across different configurations of predictors (5, 6, and 7 variables). Validation was conducted through four distinct sampling techniques to ensure robustness and reliability of the predictions.

The Bayesian network model demonstrated promising predictive performance for AF relapse. Using 5 predictors (age, sex, smoking, preablation AF type, and obstructive sleep apnea), the model achieved an AUC-ROC of 0.661 (95% CI 0.603-0.718). Incorporating additional predictors improved performance, with a 6-predictor model (adding BMI) achieving an AUC-ROC of 0.703 (95% CI 0.652-0.753) and a 7-predictor model (adding left atrial volume and epicardial fat) achieving an AUC-ROC of 0.752 (95% CI 0.701-0.800). These results indicate that the model can effectively estimate the risk of AF relapse using readily available clinical variables. Notably, the model maintained acceptable diagnostic accuracy even in scenarios where some predictive features were missing, highlighting its adaptability and potential use in real-world clinical settings.

The developed Bayesian network model provides a reliable and interpretable tool for predicting AF relapse in patients undergoing percutaneous PVI. By using easily accessible clinical variables, presenting acceptable diagnostic accuracy, and showing adaptability to incorporate new medical knowledge over time, the model demonstrates a flexibility and robustness that makes it suitable for real-world clinical scenarios.

The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart based on the FitzHugh-Nagumo (FHN) model was established for cardiac electrophysiology simulation and the ECG signals were acquired from the FEM results of representative points. Two different kinds of arrhythmia characterized by major anomalies of parameters a and ɛ in the FHN model were simulated, and the synthetic ECG signals were obtained respectively. A multi-objective optimization method based on non-dominated sorting was incorporated into the crayfish optimization algorithm to optimize the key parameters in VMD, then a variational mode decomposition technique for ECG signal processing based on a multi-objective crayfish optimization algorithm (MOCOA-VMD) was proposed, wherein the spectral kurtosis and KL divergence were determined as the indicators for decomposition. The Pareto optimal front was generated by MOCOA and the intrinsic mode functions of VMD with the best combination of K and α were obtained. A deep attention model based on MOCOA-VMD was constructed for ECG signal classification. The ablation study was implemented to verify the effectiveness of the proposed signal decomposition method and deep attention modules. The performance of the model based on MOCOA-VMD achieves the best accuracy of 94.35%, much higher than the model constructed by modules of EEMD, VMD and CNN. Moreover, Bayesian optimization was carried out to fine-tune the hyperparameters batch size, learning rate, epochs, and momentum. After TPE optimization, the deep model's performance achieved a maximum accuracy of 95.91%. The MIT-BIH arrhythmia database was further utilized for model validation, ascertaining its robustness and generalizability. The proposed deep attention modeling and classification strategy can help in arrhythmia signal processing and may offer inspiration for other signal processing fields as well.

Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA).

This scoping review aimed to evaluate the current scientific evidence on the application of ML for managing patients undergoing AFCA, compare the performance of various models across specific clinical tasks within AFCA, and summarize the strengths and limitations of ML in this field.

Adhering to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, relevant studies published up to October 7, 2023, were searched from PubMed, Web of Science, Embase, the Cochrane Library, and ScienceDirect. The final included studies were confirmed based on inclusion and exclusion criteria and manual review. The PROBAST (Prediction model Risk Of Bias Assessment Tool) and QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) methodological quality assessment tools were used to review the included studies, and narrative data synthesis was performed on the modeled results provided by these studies.

The analysis of 23 included studies showcased the contributions of ML in identifying potential ablation targets, improving ablation strategies, and predicting patient prognosis. The patient data used in these studies comprised demographics, clinical characteristics, various types of imaging (9/23, 39%), and electrophysiological signals (7/23, 30%). In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). Compared with traditional clinical scoring models or human clinicians, the model performance reported in the included studies was generally satisfactory, but most models (14/23, 61%) showed a high risk of bias due to lack of external validation.

Our evidence-based findings suggest that ML is a promising tool for improving the effectiveness and efficiency of managing patients undergoing AFCA. While guiding data preparation and model selection for future studies, this review highlights the need to address prevalent limitations, including lack of external validation, and to further explore model generalization and interpretability.

Hip fracture and acute ischemic stroke (AIS) are prevalent conditions among the older population. The prognosis for older patients who experience AIS subsequent to hip fracture is frequently unfavorable.

Patients were categorized into the AIS group and the non-AIS group. A predictive model was developed using six different machine learning algorithms. The SHapley Additive exPlanations (SHAP) method was then utilized to provide both local and global explanations. We performed adjusted mediation analyses. Furthermore, a nomogram was created to present the outcomes obtained from the LASSO regression examination. The main objective was to ascertain influential elements that can predict the occurrence of AIS. To alleviate the influence of confounding variables, propensity score matching was utilized to compare the occurrence of additional complications. Survival was compared by Kaplan-Meier methods.

The AUC of 6 ML models ranged from 0.73 to 0.87. The SVM model exhibited the greatest efficacy in forecasting AIS among older individuals with hip fractures. The leading 6 variables in the support vector machines (SVM) model were identified as systemic inflammatory response index (SIRI), carotid atherosclerosis, prior stroke, C-reactive protein (CRP), fibrinogen (FIB), and hypertension. The leading 2 variables in SHAP were identified as FIB at admission and SIRI index. There wasn't potential mediating effect of admission FIB between the SIRI index and AIS. There were statistically significant differences between the two groups in survival (P=0.003).

The model displayed good performance for prediction of AIS after hip fracture in patients 65 years and older, which might facilitate to establishment of a better clinical assessment plan.

Atrial fibrillation (AF) is a condition with heterogeneous underlying causes, often involving multiple cardiovascular comorbidities. Large-scale studies examining the heterogeneity of patients with AF in the Asian population are limited.

The purpose of this study was to identify distinct phenotypic clusters of patients with AF and evaluate their associated risks of ischemic stroke, heart failure hospitalization, cardiovascular mortality, and all-cause mortality.

We analyzed 5002 adult patients with AF from the National Taiwan University Hospital between 2014 and 2019 using an unsupervised hierarchical cluster analysis based on the CHA2DS2-VASc score.

We identified 4 distinct groups of patients with AF: cluster I included diabetic patients with heart failure preserved ejection fraction as well as chronic kidney disease (CKD); cluster II comprised older patients with low body mass index and pulmonary hypertension; cluster III consisted of patients with metabolic syndrome and atherosclerotic disease; and cluster IV comprised patients with left heart dysfunction, including reduced ejection fraction. Differences in the risk of ischemic stroke across clusters (clusters I, II, and III vs cluster IV) were statistically significant (hazard ratio [HR] 1.87, 95% confidence interval [CI] 1.00-3.48; HR 2.06, 95% CI 1.06-4.01; and HR 1.70, 95% CI 1.02-2.01). Cluster II was independently associated with the highest risk of hospitalization for heart failure (HR 1.19, 95% CI 0.79-1.80), cardiovascular mortality (HR 2.51, 95% CI 1.21-5.22), and overall mortality (HR 2.98, 95% CI 1.21-4.2).

A data-driven algorithm can identify distinct clusters with unique phenotypes and varying risks of cardiovascular outcomes in patients with AF, enhancing risk stratification beyond the CHA2DS2-VASc score.

The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results. The latent space ECG model identified associations with 645 prevalent and 606 incident Phecodes. Associations were most enriched in the circulatory (n = 140, 82% of category-specific Phecodes), respiratory (n = 53, 62%) and endocrine/metabolic (n = 73, 45%) categories, with additional associations across the phenome. The strongest ECG association was with hypertension (p < 2.2×10-308). The ECG latent space model demonstrated more associations than models using standard ECG intervals, and offered favorable discrimination of prevalent disease compared to models comprising age, sex, and race. We further demonstrate how latent space models can be used to generate disease-specific ECG waveforms and facilitate individual disease profiling.

The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.

We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF). The generalization ability of the model is validated on public datasets CPSC2018, PhysioNet2017, and PTB-XL, and we explored the performance of oversampling, resampling, and hybrid datasets. Finally, additional PhysioNet2021 was added to validate the robustness and applicability in different clinical settings. We employed the SHapley Additive exPlanations (SHAP) method to interpret the model's predictions. The F1-score, Precision, and area under the ROC curve (AUC) of the CLA-AF model on YY2023 are 0.956, 0.970, and 1.00, respectively. Similarly, the AUC on CPSC2018, PhysioNet2017, and PTB-XL reached above 0.95, demonstrating its strong generalization ability. After oversampling PhysioNet2017, F1-score and Recall improved by 0.156 and 0.260. Generalization ability varied with sampling frequency. The model trained from the hybrid dataset has the most robust generalization ability, achieving an AUC of 0.96 or more. The AUC of PhysioNet2021 is 1.00, which proves the applicability of CLA-AF. The SHAP values visualization results demonstrate that the model's interpretation of AF aligns with the diagnostic criteria of AF.

The CLA-AF model demonstrates a high accuracy in recognizing AF from ECG, exhibiting remarkable applicability and robustness in diverse clinical settings.

Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and accurate diagnosis of heart pathologies. This is due to the growing need to improve efficiency in diagnostics and hasten the process of delivering treatment. Several institutions have actively assessed the possibility of creating algorithms for advancing our understanding of atrial fibrillation (AF), a common form of sustained arrhythmia. This means that artificial intelligence is now being used to analyze electrocardiogram (ECG) data. The data is typically extracted from large patient databases and then subsequently used to train and test the algorithm with the help of neural networks. Machine learning has been used to effectively detect atrial fibrillation with more accuracy than clinical experts, and if applied to clinical practice, it will aid in early diagnosis and management of the condition and thus reduce thromboembolic complications of the disease. In this text, a review of the application of machine learning in the analysis and detection of atrial fibrillation, a comparison of the outcomes (sensitivity, specificity, and accuracy), and the framework and methods of the studies conducted have been presented.

Cardiovascular diseases resulting from myocardial infarction (MI) remain a leading cause of death worldwide, imposing a substantial burden on global health systems. Current MI treatments, primarily pharmacological and surgical, do not regenerate lost myocardium, leaving patients at high risk for heart failure. Engineered heart tissue (EHT) offers a promising solution for MI and related cardiac conditions by replenishing myocardial loss. However, challenges like immune rejection, inadequate vascularization, limited mechanical strength, and incomplete tissue maturation hinder clinical application. The discovery of human-induced pluripotent stem cells (hiPSCs) has transformed the EHT field, enabling new bioengineering innovations. This review explores recent advancements and future directions in hiPSC-derived EHTs, focusing on innovative materials and fabrication methods like bioprinting and decellularization, and assessing their therapeutic potential through preclinical and clinical studies. Achieving functional integration of EHTs in the heart remains challenging due to the need for synchronized contraction, sufficient vascularization, and mechanical compatibility. Solutions such as genome editing, personalized medicine, and AI technologies offer promising strategies to address these translational barriers. Beyond MI, EHTs also show potential in treating ischemic cardiomyopathy, heart valve engineering, and drug screening, underscoring their promise in cardiovascular regenerative medicine.

This review aims to explore the emerging potential of artificial intelligence (AI) in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM).

Recent developments highlight the capacity of AI to construct sophisticated models that accurately distinguish affected from non-affected cardiomyopathy patients. These AI-driven approaches not only offer precision in risk prediction and diagnostics but also enable early identification of individuals at high risk of developing cardiomyopathy, even before symptoms occur. These models have the potential to utilise diverse clinical input datasets such as electrocardiogram recordings, cardiac imaging, and other multi-modal genetic and omics datasets. Despite their current underrepresentation in literature, ACM diagnosis and risk prediction are expected to greatly benefit from AI computational capabilities, as has been the case for other cardiomyopathies. As AI-based models improve, larger and more complicated datasets can be combined. These more complex integrated datasets with larger sample sizes will contribute to further pathophysiological insights, better disease recognition, risk prediction, and improved patient outcomes.

Ventricular arrhythmias increase cardiovascular morbidity and mortality. Recurrent PVCs and IVT are generally considered benign in the absence of structural heart abnormalities. Artificial intelligence is a rapidly growing field. In recent years, medical professionals have shown great interest in the potential use of ML, an integral part of AI, in various disciplines, including diagnostic applications, decision-making, prognostic stratification, and solving complex pathophysiological aspects of diseases from these data at extraordinary complexity, scale, and acquisition rate. The aim of this study was to design an ML model to predict the probability of PVC and IVT recurrence after RF ablation. Data of patients were collected and manipulated using traditional analysis and various artificial intelligence models, namely MLP, Gradient Boosting Machines, Random Forest, and Logistic Regression. Hypertension, male sex, and the use of non-irrigate catheters were associated with less freedom from arrhythmia. All these results were obtained through traditional analytic methods, and according to AI, none of the variables had a clear effect on the recurrence of arrhythmia. Each AI model presents unique strengths and weaknesses, and further optimization and fine-tuning of these models are necessary to increase their clinical utility. By expanding the dataset, improved predictions can be fostered to ultimately increase the clinical utility of AI in predicting PVC erosion outcomes.

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI's potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI's full potential in improving myocarditis care.

We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET) and 17,649 ECG-single-photon emission computed tomography (SPECT) data pairs, the ML model is trained with a swarm intelligence approach and support vector regression (SVR). The model achieves a receiver-operator curve (ROC) area under the curve (AUC) of 0.83, with a sensitivity and specificity of 0.75. An ECG-MFR value below 2 is significantly associated with MACE, with hazard ratios (HRs) of 3.85 and 3.70 in the discovery and validation phases, respectively. The model's C-statistic is 0.76, with a net reclassification improvement (NRI) of 0.35. Validated in an independent cohort, the ML model using ECG data offers superior MACE prediction compared to baseline clinical models, highlighting its potential for risk stratification in patients with coronary artery disease (CAD) using the accessible 12-lead ECG.

CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain.

We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non-PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated.

A total of 29,422 points were analyzed (PV lesions [n = 22 418], non-PV lesions [n = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non-PV lesions (PV lesions vs. non-PV lesions, %; sensitivity, 75.3 vs. 67.5, p < .05; PPV, 91.2 vs. 67.9, p < .05). CTI and superior vena cava could not be recognized or analyzed. In the potential reconnection prediction model, the incidence of potential reconnections was highest in the posterior, while the confidence was the highest in the roof.

The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites.

Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients' and healthcare providers' perspectives when using AI in cardiovascular care.

In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients', caregivers', or healthcare providers' perspectives. The search was completed on May 24, 2022 and was not limited by date or study design.

After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients' interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights.

This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients' and healthcare providers' perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.

Atrial fibrosis has a significant impact on the success rate of catheter ablation (CA) treatment of atrial fibrillation (AF). The fibrotic tissues could be reflected by the amplitude of the fibrillatory wave (F-wave).

704 patients with persistent AF and at least 1-year follow-up after CA were included as the internal group. 101 patients from another hospital were used as the external validation cohort. A 12‑lead ECG was performed before CA and the maximum FWA in three ECG leads (aVL, aVF, V1) were measured. The FWA score (0 to 6 points according to the amplitude range of the three leads) of each patients was calculated. Five models including clinical features, FWA score, CHA2DS2-VASc score, APPLE score and the fusion of clinical features and FWA score were built. The FWA score was superior to the model constructed by clinical variables, CHA2DS2-VASc score and APPLE score. It not only had good predictive performance for AF recurrence, with an AUC value of 0.812 (95% CI 0.724-0.900), but also showed a significant predictive value for the recurrence rate according to F-wave amplitude. In the external validation cohort, the FWA score showed similar results (AUC 0.768, 95% CI 0.672-0.865).

The present study reveals the significant predictive value of the FWA score for persistent AF ablation recurrence.

Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities.

A total of 278 patients with WQCT from January 2010 to March 2022 were enrolled. The electrophysiological study confirmed SVT in 154 patients and VT in 65 ones. Two hundred nineteen WQCT 12-lead ECGs were randomly divided into development cohort (n = 165) and testing cohort (n = 54) data sets. The development cohort was split into a training group (n = 115) and an internal validation group (n = 50). Forty ECG features extracted from the 219 WQCT ECGs are fed into 9 iteratively trained ML algorithms. This novel ML algorithm was also compared with four published algorithms.

In the development cohort, the Gradient Boosting Machine (GBM) model displayed the maximum area under curve (AUC) (0.91, 95% confidence interval (CI) 0.81-1.00). In the testing cohort, the GBM model had a higher AUC of 0.97 compared to 4 validated ECG algorithms, namely, Brugada (0.68), avR (0.62), RWPTII (0.72), and LLA algorithms (0.70). Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the GBM model were 0.94, 0.97, 0.90, 0.94, and 0.95, respectively.

A GBM ML model contributes to distinguishing SVT from VT based on surface ECG features. In addition, we were able to identify important indicators for distinguishing WQCT.

This study sought to develop an unbalanced-ensemble model that could accurately predict death outcomes of patients with comorbid coronary heart disease (CHD) and hypertension and evaluate the factors contributing to death.

Medical records of 1058 patients with coronary heart disease combined with hypertension and excluding those acute coronary syndrome were collected. Patients were followed-up at the first, third, sixth, and twelfth months after discharge to record death events. Follow-up ended two years after discharge. Patients were divided into survival and nonsurvival groups. According to medical records, gender, smoking, drinking, COPD, cerebral stroke, diabetes, hyperhomocysteinemia, heart failure and renal insufficiency of the two groups were sorted and compared and other influencing factors of the two groups, feature selection was carried out to construct models. Owing to data unbalance, we developed four unbalanced-ensemble prediction models based on Balanced Random Forest (BRF), EasyEnsemble, RUSBoost, SMOTEBoost and the two base classification algorithms based on AdaBoost and Logistic. Each model was optimised using hyperparameters based on GridSearchCV and evaluated using area under the curve (AUC), sensitivity, recall, Brier score, and geometric mean (G-mean). Additionally, to understand the influence of variables on model performance, we constructed a SHapley Additive explanation (SHAP) model based on the optimal model.

There were significant differences in age, heart rate, COPD, cerebral stroke, heart failure and renal insufficiency in the nonsurvival group compared with the survival group. Among all models, BRF yielded the highest AUC (0.810; 95% CI, 0.778-0.839), sensitivity (0.990; 95% CI, 0.981-1.000), recall (0.990; 95% CI, 0.981-1.000), and G-mean (0.806; 95% CI, 0.778-0.827), and the lowest Brier score (0.181; 95% CI, 0.178-0.185). Therefore, we identified BRF as the optimal model. Furthermore, red blood cell count (RBC), body mass index (BMI), and lactate dehydrogenase were found to be important mortality-associated risk factors.

BRF combined with advanced machine learning methods and SHAP is highly effective and accurately predicts mortality in patients with CHD comorbid with hypertension. This model has the potential to assist clinicians in modifying treatment strategies to improve patient outcomes.

Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG.

The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites.

Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis.

The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Ablation of scar-related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well-suited to analyze the irregularly-structured data obtained in mapping procedures and may be used to identify potential isthmuses.

Electroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off-line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas.

A total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group.

A GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets.

Digital precision medicine is gaining increasing importance in rhythmology, especially in the treatment of cardiac arrhythmias. This trend is driven by the advancing digitization in healthcare and the availability of large amounts of data from various sources such as electrocardiograms (ECGs), implants like pacemakers and implantable cardioverter-defibrillators (ICDs), as well as wearables like smartwatches and fitness trackers. Through the analysis of this data, physicians can develop more precise and individualized diagnoses and treatment strategies for patients with cardiac arrhythmias. For example, subtle changes in ECGs can be identified, indicating potentially dangerous arrhythmias. Genetic analyses and resulting large datasets also play an increasingly significant role, especially in hereditary ion channel disorders such as long QT syndrome (LQTS) and Brugada syndrome (BrS), as well as in lone atrial fibrillation (AF). Precision medicine enables the development of individualized treatment approaches tailored to the specific needs and risk factors of each patient. This can help improve screening strategies, reduce adverse events, and ultimately enhance the quality of life for patients. Technological advancements such as big data, artificial intelligence, machine learning, and predictive analytics play a crucial role in predicting the risk of arrhythmias and sudden cardiac death. These concepts enable more precise and personalized predictions and support physicians in the treatment and monitoring of their patients.

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.