Despite the proliferation of heart failure (HF) mortality prediction models, their practical utility is limited. Addressing this, we utilized a significant dataset to develop and validate a deep learning artificial intelligence (AI) model for predicting one-year mortality in heart failure with reduced ejection fraction (HFrEF) patients. The study’s focus was to assess the effectiveness of an AI algorithm, trained on an extensive collection of ECG data, in predicting one-year mortality in HFrEF patients. We selected HFrEF patients who had high-quality baseline ECGs from two hospital visits between September 2016 and May 2021. A total of 3,894 HFrEF patients (64% male, mean age 64.3, mean ejection fraction 29.8%) were included. Using this ECG data, we developed a deep learning model and evaluated its performance using the area under the receiver operating characteristic curve (AUROC). The model, validated against 16,228 independent ECGs from the original cohort, achieved an AUROC of 0.826 (95 % CI, 0.794–0.859). It displayed a high sensitivity of 99.0 %, positive predictive value of 16.6 %, and negative predictive value of 98.4 %. Importantly, the deep learning algorithm emerged as an independent predictor of 1-yr mortality of HFrEF patients with an adjusted hazards ratio of 4.12 (95 % CI 2.32–7.33, p < 0.001). The depth and quality of our dataset and our AI-driven ECG analysis model significantly enhance the prediction of one-year mortality in HFrEF patients. This promises a more personalized, future-focused approach in HF patient management.

Emerging evidence supports artificial intelligence–enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of AI-ECG in detecting AMI in the emergency department (ED). The Rule-Out acute Myocardial Infarction using Artificial intelligence Electrocardiogram analysis (ROMIAE) study is a prospective cohort study conducted in the Republic of Korea from March 2022 to October 2023, involving 18 university-level teaching hospitals. Adult patients presenting to the ED within 24 h of symptom onset concerning for AMI were assessed. Exposure included AI-ECG score, HEART score, GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. The primary outcome was diagnosis of AMI during index admission, and the secondary outcome was 30 day major adverse cardiovascular event (MACE). The study population comprised 8493 adults, of whom 1586 (18.6%) were diagnosed with AMI. The area under the receiver operating characteristic curve for AI-ECG was 0.878 (95% CI, 0.868–0.888), comparable with the HEART score (0.877; 95% CI, 0.869–0.886) and superior to the GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. For predicting 30 day MACE, AI-ECG (area under the receiver operating characteristic, 0.866; 95% CI, 0.856–0.877) performed comparably with the HEART score (0.858; 95% CI, 0.848–0.868). The integration of the AI-ECG improved risk stratification and AMI discrimination, with a net reclassification improvement of 19.6% (95% CI, 17.38–21.89) and a C-index of 0.926 (95% CI, 0.919–0.933), compared with the HEART score alone. In this multicentre prospective study, the AI-ECG demonstrated diagnostic accuracy and predictive power for AMI and 30 day MACE, which was similar to or better than that of traditional risk stratification methods and ED physicians.

This study explores the use of artificial intelligence-enabled electrocardiogram (AI-ECG) technology combined with smartwatch-based daily monitoring to predict heart failure (HF) rehospitalization by detecting early signs of heart failure exacerbation, such as left ventricular systolic dysfunction (LVSD), left ventricular diastolic dysfunction (LVDD), and myocardial infarction (MI). Traditional monitoring methods have limitations, and AI-ECG offers a scalable, noninvasive, cost-effective solution. The study will evaluate the impact of this technology on reducing rehospitalization rates in a prospective, multicenter trial involving 220 adult patients recently discharged after HF hospitalization. The primary endpoint is a reduction in HF rehospitalization rates within 90 days, with secondary endpoints including time to clinical intervention, unplanned hospitalizations, and improvements in mortality and quality of life. This approach represents a promising, patient-friendly solution for better HF management.

Heart failure (HF) is an escalating global health concern, worsened by an aging population and limitations in traditional diagnostic methods like electrocardiograms (ECG). The advent of deep learning has shown promise for utilizing 12-lead ECG models for the early detection of left ventricular systolic dysfunction (LVSD), a crucial HF indicator. This study validates the AiTiALVSD, an AI/machine learning-enabled Software as a Medical Device, for its effectiveness, transparency, and robustness in detecting LVSD. Conducted at Mediplex Sejong Hospital in the Republic of Korea, this retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, which is based on a deep learning algorithm, was assessed against echocardiography findings. To improve model transparency, the study utilized Testing with Concept Activation Vectors (TCAV) and included clustering analysis and robustness tests against ECG noise and lead reversals. The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model’s predictive accuracy. TCAV analysis showed the model’s alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. AiTiALVSD’s high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI/ML-based diagnostics, setting a new benchmark in cardiac care.

The application of artificial intelligence (AI) to electrocardiograms (ECGs) has shown great promise in the screening and diagnosis of cardiovascular diseases, often matching or surpassing human expertise. However, the “black-box” nature of deep learning models poses significant challenges to their clinical adoption. While Explainable AI (XAI) techniques, such as Saliency Maps, have attempted to address these issues, they have not been able to provide clear, clinically relevant explanations. We developed the Generative Counterfactual ECG XAI (GCX) framework, which uses counterfactual scenarios to explain AI predictions, enhancing interpretability and aligning with medical knowledge. We designed a study to validate the GCX framework by applying it to eight AI-ECG models, including those focused on regression of six ECG features, potassium level regression, and atrial fibrillation (AF) classification. PTB-XL and MIMIC-IV were used to develop and test. GCX generated counterfactual (CF) ECGs to visualize how changes in the ECG relate to AI-ECG predictions. We visualized CF ECGs for qualitative comparisons, statistically compared ECG features, and validated these findings with conventional ECG knowledge. The GCX framework successfully generated interpretable ECGs aligned with clinical knowledge, particularly in the context of ECG feature regression, potassium level regression, and AF classification. For ECG feature regression, GCX demonstrated clear and consistent changes in features, reflecting the corresponding morphological alterations. CF ECGs for hyperkalemia showed a prolonged PR, discernible P wave, increased T wave amplitude, and widened QRS complex, whereas those for AF demonstrated the disappearance of the P wave and irregular rhythms. The GCX framework enhances the interpretability of AI-ECG models, offering clear relevant explanations for AI predictions. This approach holds substantial potential for improving the trust and utility of AI in clinical practice, although further validation across diverse datasets is required.

Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting left ventricular systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR). Methods and results This study is an external validation of a pre-existing deep-learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF ≤40%,assessed using 12-lead electrocardiography. Secondary outcome involved predicting LVSD using 1-lead electrocardiography (lead I). Among 423 patients, 241 with available echocardiography data within 2 months were evaluated, of whom 54 (22.4%) were confirmed to have LVSD. Deep-learning algorithm demonstrated fair performance in predicting LVSD (area under the curve [AUC] 0.78). Negative predictive value for excluding LVSD was 0.88. Deep-learning algorithm resulted competent performance in predicting LVSD compared to N-terminal prohormone of brain natriuretic peptide (AUC 0.78 vs. 0.70, p=0.12). Predictive performance of the deep-learning algorithm was lower in 1-lead electrocardiography (AUC 0.68), however, negative predictivevalue remained consistent (0.88). Conclusion Deep-learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence-based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR

Domain generalization involves training machine learning models to perform robustly on unseen samples from out-of-distribution datasets. Adversarial Data Augmentation (ADA) is a commonly used approach that enhances model adaptability by incorporating synthetic samples, designed to simulate potential unseen samples. While ADA effectively addresses amplitude-related distribution shifts, it falls short in managing temporal shifts, which are essential for time series data. To address this limitation, we propose the Temporal Adversarial Data Augmentation for time teries Data (TADA), which incorporates a time warping technique specifically targeting temporal shifts. Recognizing the challenge of non-differentiability in traditional time warping, we make it differentiable by leveraging phase shifts in the frequency domain. Our evaluations across diverse domains demonstrate that TADA significantly outperforms existing ADA variants,enhancing model performance across time series datasets with varied distributions.

Foundation models, enhanced by self-supervised learning (SSL) techniques, represent a cutting-edge frontier in biomedical signal analysis, particularly for electrocardiograms (ECGs), crucial for cardiac health monitoring and diagnosis. This study conducts a comprehensive analysis of foundation models for ECGs by employing and refining innovative SSL methodologies - namely, generative and contrastive learning - on a vast dataset of over 1.1 million ECG samples. By customizing these methods to align with the intricate characteristics of ECG signals, our research has successfully developed foundation models that significantly elevate the precision and reliability of cardiac diagnostics. These models are adept at representing the complex, subtle nuances of ECG data, thus markedly enhancing diagnostic capabilities. Theresults underscore the substantial potential of SSL-enhanced foundation models in clinical settings and pave the way for extensive future investigations into their scalable applications across a broader spectrum of medical diagnostics. This work sets a benchmark in the ECG field, demonstrating the profound impact of tailored, data-driven model training on the efficacy and accuracy of medical diagnostics.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.

Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department. Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board. This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments.