Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.

Role of the Electrocardiogram for Identifying the Development of Atrial Fibrillation

Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity and mortality worldwide. Stroke, the leading cause of serious disability in the United States, is among the important complications of this arrhythmia. Recent studies have demonstrated that certain clinical variables can be useful in the prediction of AF development in the future. The electrocardiogram (ECG) is a simple and cost-effective technology that is widely available in various healthcare settings. An emerging body of evidence has suggested that ECG tracings preceding the development of AF can be useful in predicting this arrhythmia in the future. Various variables on ECG especially different P wave parameters have been investigated in the prediction of new-onset AF and found to be useful. Several risk models were also introduced using these variables along with the patient's clinical data. However, current guidelines do not provide a clear consensus regarding implementing these prediction models in clinical practice for identifying patients at risk of AF. Also, the role of intensive screening via ECG or implantable devices based on this scoring system is unclear. The purpose of this review is to summarize AF and various related terminologies and explain the pathophysiology and electrocardiographic features of this tachyarrhythmia. We also discuss the predictive electrocardiographic features of AF, review some of the existing risk models and scoring system, and shed light on the role of monitoring device for screening purposes.

Chemotherapy Related Cardiotoxicity Evaluation-A Contemporary Review with a Focus on Cardiac Imaging

The long-term survivorship of patients diagnosed with cancer has improved due to accelerated detection and rapidly evolving cancer treatment strategies. As such, the evaluation and management of cancer therapy related complications has become increasingly important, including cardiovascular complications. These have been captured under the umbrella term "cardiotoxicity" and include left ventricular dysfunction and heart failure, acute coronary syndromes, valvular abnormalities, pericardial disease, arrhythmia, myocarditis, and vascular complications. These complications add to the burden of cardiovascular disease (CVD) or are risk factors patients with cancer treatment are presenting with. Of note, both pre- and newly developing CVD is of prognostic significance, not only from a cardiovascular perspective but also overall, potentially impacting the level of cancer therapy that is possible. Currently, there are varying recommendations and practices regarding CVD risk assessment and mitigating strategies throughout the cancer continuum. This article provides an overview on this topic, in particular, the role of cardiac imaging in the care of the patient with cancer. Furthermore, it summarizes the current evidence on the spectrum, prevention, and management of chemotherapy-related adverse cardiac effects.

Outcomes of Device-detected Atrial High-rate Episodes in Patients with No Prior History of Atrial Fibrillation: A Systematic Review and Meta-analysis

Background

Outcomes of device-detected AF remain unclear in individuals without a prior history of AF.

Methods

A meta-analysis was conducted to evaluate outcomes in individuals with no prior history of AF who experienced device-detected AF. Outcomes assessed were clinical AF, thromboembolism and all-cause mortality. A fixed-effects model was used to calculate RRs with 95% CI.

Results

Compared to individuals who did not experience device-detected AF, those who did had increased risks of clinical AF (RR 3.33, 95% CI [1.99.5.57]; p<0.0001) and thromboembolic events (RR 2.21; 95% CI [1.72.2.85]; p<0.0001). The risk of all-cause mortality was similar between both groups (RR 1.19; 95% CI [0.95.1.49]; p=0.13). Subgroup analysis revealed an increased risk of thromboembolic events among device-detected AF .24 hours (RR 12.34; 95% CI [2.70.56.36]).

Conclusion

While there is an increased risk of clinical AF and thromboembolism in individuals with device-detected AF, mortality was insignificant.

Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review

Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. Following the PRISMA recommendations, this systematic review outlines ML models employed in the prediction and detection of NOAF in ICU patients and compares the ML-based approach with clinical-based methods. Inclusion criteria comprised randomized controlled trials (RCTs), observational studies, cohort studies, and case-control studies. A total of five articles published between November 2020 and April 2023 were identified and reviewed to extract the algorithms and performance metrics. Reviewed studies sourced 108,724 ICU admission records form databases, e.g., MIMIC. Eight prediction and detection methods were examined. Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.

Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review

Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular health. Cardiovascular concerns, including sudden cardiac death, pose significant risks during sports activities. Traditional ECG, essential yet limited, often fails to distinguish between benign cardiac adaptations and serious conditions. This narrative review investigates the application of machine learning (ML) and deep learning (DL) in ECG interpretation, aiming to improve the detection of arrhythmias, channelopathies, and hypertrophic cardiomyopathies. A literature review over the past decade, sourcing from PubMed and Google Scholar, highlights the growing adoption of AI in sports medicine for its precision and predictive capabilities. AI algorithms excel at identifying complex cardiac patterns, potentially overlooked by traditional methods, and are increasingly integrated into wearable technologies for continuous monitoring. Overall, by offering a comprehensive overview of current innovations and outlining future advancements, this review supports sports medicine professionals in merging traditional screening methods with state-of-the-art AI technologies. This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs.

The Clinical Applications of Left Atrial Strain: A Comprehensive Review

Left atrial (LA) strain imaging, which measures the deformation of the LA using speckle-tracing echocardiography (STE), has emerged recently as an exciting tool to help provide diagnostic and prognostic information for patients with a broad range of cardiovascular (CV) pathologies. Perhaps due to the LA's relatively thin-walled architecture compared with the more muscular structure of the left ventricle (LV), functional changes in the left atrium often precede changes in the LV, making LA strain (LAS) an earlier marker for underlying pathology than many conventional echocardiographic parameters. LAS imaging is typically divided into three phases according to the stage of the cardiac cycle: reservoir strain, which is characterized by LA filling during systole; conduit strain, which describes LA deformation during passive LV filling; and booster strain, which provides information on the LA atrium during LA systole in late ventricular diastole. While additional large-population studies are still needed to further solidify the role of LAS in routine clinical practice, this review will discuss the current evidence of its use in different pathologies and explore the possibilities of its applications in the future.

Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.

CT-Assessment of Epicardial Fat Identifies Increased Inflammation at the Level of the Left Coronary Circulation in Patients with Atrial Fibrillation

Background: Atrial fibrillation (AF) can often be triggered by an inflammatory substrate. Perivascular inflammation may be assessed nowadays using coronary computed tomography angiography (CCTA) imaging. The new pericoronary fat attenuation index (FAI HU) and the FAI Score have prognostic value for predicting future cardiovascular events. Our purpose was to investigate the correlation between pericoronary fat inflammation and the presence of AF among patients with coronary artery disease. Patients and methods: Eighty-one patients (mean age 64.75 ± 7.84 years) who underwent 128-slice CCTA were included in this study and divided into two groups: group 1 comprised thirty-six patients with documented AF and group 2 comprised forty-five patients without a known history of AF. Results: There were no significant differences in the absolute value of fat attenuation between the study groups (p > 0.05). However, the mean FAI Score was significantly higher in patients with AF (15.53 ± 10.29 vs. 11.09 ± 6.70, p < 0.05). Regional analysis of coronary inflammation indicated a higher level of this process, especially at the level of the left anterior descending artery (13.17 ± 7.91 in group 1 vs. 8.80 ± 4.75 in group 2, p = 0.008). Conclusions: Patients with AF present a higher level of perivascular inflammation, especially in the region of the left coronary circulation, and this seems to be associated with a higher risk of AF development.

Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head-to-Head Comparison With Hospital-Based Physician ECG Readers

BACKGROUND

Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high-risk patients is important but labor-intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development presents challenges.

METHODS AND RESULTS

We used a novel approach to AI development for AF detection using both surface ECG recordings and atrial epicardial electrograms obtained in postoperative cardiac patients. Atrial electrograms were used only to facilitate establishing true AF for AI development; this permitted the establishment of an AI-based tool for subsequent AF detection using ECG records alone. A total of 5 million 30-second epochs from 329 patients were annotated as AF or non-AF by expert ECG readers for AI training and validation, while 5 million 30-second epochs from 330 different patients were used for AI testing. AI performance was assessed at the epoch level as well as AF burden at the patient level. AI achieved an area under the receiver operating characteristic curve of 0.932 on validation and 0.953 on testing. At the epoch level, testing results showed means of AF detection sensitivity, specificity, negative predictive value, positive predictive value, and F1 (harmonic mean of positive predictive value and sensitivity) as 0.970, 0.814, 0.976, 0.776, and 0.862, respectively, while the intraclass correlation coefficient for AF burden detection was 0.952. At the patient level, AF burden sensitivity and positive predictivity were 96.2% and 94.5%, respectively.

CONCLUSIONS

Use of both atrial electrograms and surface ECG permitted development of a robust AI-based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery.

Towards a new classification of atrial fibrillation detected after a stroke or a transient ischaemic attack

Globally, up to 1·5 million individuals with ischaemic stroke or transient ischaemic attack can be newly diagnosed with atrial fibrillation per year. In the past decade, evidence has accumulated supporting the notion that atrial fibrillation first detected after a stroke or transient ischaemic attack differs from atrial fibrillation known before the occurrence of as stroke. Atrial fibrillation detected after stroke is associated with a lower prevalence of risk factors, cardiovascular comorbidities, and atrial cardiomyopathy than atrial fibrillation known before stroke occurrence. These differences might explain why it is associated with a lower risk of recurrence of ischaemic stroke than known atrial fibrillation. Patients with ischaemic stroke or transient ischaemic attack can be classified in three categories: no atrial fibrillation, known atrial fibrillation before stroke occurrence, and atrial fibrillation detected after stroke. This classification could harmonise future research in the field and help to understand the role of prolonged cardiac monitoring for secondary stroke prevention with application of a personalised risk-based approach to the selection of patients for anticoagulation.

Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study

Introduction

Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital. The secondary objective is to investigate whether the AI-enhanced ECG is associated with AF-related clinical outcomes.

Methods and analysis

We will conduct a retrospective cohort study of more than 50,000 12-lead ECGs from November 1, 2012, to December 31, 2021, at 10 Korean University Hospitals. Data will be collected from patient records, including baseline demographics, comorbidities, laboratory findings, echocardiographic findings, hospitalizations, and related procedural outcomes, such as AF ablation and mortality. De-identification of ECG data through data encryption and anonymization will be conducted and the data will be analyzed using the AI algorithm previously developed for AF prediction. An area under the receiver operating characteristic curve will be created to test and validate the datasets and assess the AI-enabled ECGs acquired during the sinus rhythm to determine whether AF is present. Kaplan-Meier survival functions will be used to estimate the time to hospitalization, AF-related procedure outcomes, and mortality, with log-rank tests to compare patients with low and high risk of AF by AI. Multivariate Cox proportional hazards regression will estimate the effect of AI-enhanced ECG multimorbidity on clinical outcomes after stratifying patients by AF probability by AI.

Discussion

This study will advance PAF prediction based on AI-enhanced ECGs. This approach is a novel method for risk stratification and emphasizes shared decision-making for early detection and management of patients with newly diagnosed AF. The results may revolutionize PAF management and unveil the wider potential of AI in predicting and managing cardiovascular diseases.

Ethics and dissemination

The study findings will be published in peer-reviewed publications and disseminated at national and international conferences and through social media. This study was approved by the institutional review boards of all participating university hospitals. Data extraction, storage, and management were approved by the data review committees of all institutions.

Clinical Trial Registration

[cris.nih.go.kr], identifier (KCT0007881).