Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation

Christopoulos, Georgios; Graff‐Radford, Jonathan; Lopez, Camden L.; Yao, Xiaoxi; Attia, Zachi I.; Rabinstein, Alejandro A.; Petersen, Ronald C.; Knopman, David S.; Mielke, Michelle M.; Kremers, Walter K.; Vemuri, Prashanthi; Siontis, Konstantinos C.; Friedman, Paul A.; Noseworthy, Peter A.

doi:10.1161/circep.120.009355

Cited by 81 publications

(64 citation statements)

References 17 publications

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“…The use of DNNs to process ECGs with normal sinus rhythm and assess the risk of developing future AF has been investigated in previous studies achieving good results 23 – 25 . However, in these previous studies there is a lack of analysis and comparison of different AI approaches 23 , 24 . Also, the prevalence of AF in adults is significantly related to age, while its relationship with sex is mostly unclear 26 – 28 .…”

Section: Introductionmentioning

confidence: 99%

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

Melzi

Tolosana

Cecconi

et al. 2021

Sci Rep

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Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.

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Section: Introductionmentioning

confidence: 99%

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

Melzi

Tolosana

Cecconi

et al. 2021

Sci Rep

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“…Other less common approaches also include the development of an ML model to predict future AF among patients with no history of AF, by Christopoulos et al ., 92 with results independently corroborated using Cox regression. Chua et al .…”

Section: ML For Detecting Afmentioning

confidence: 76%

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Olier

Ortega‐Martorell

Pieroni

et al. 2021

Cardiovascular Research

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There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centered on applying ML for detecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modeling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centered in aims such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g., blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

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“…The electrocardiogram (ECG), a rapid, inexpensive, and non-invasive diagnostic test suitable for repetitive recordings, is an ideal target for ML augmentation, especially in light of associations observed between ECG abnormalities and adverse COVID-19 outcomes. 14 , 15 ECG ML applications have been shown to recognize subtle patterns in the electrical signals, imperceptible to human readers, that can be leveraged to predict and classify different arrhythmic conditions, including AF, 16 and to screen for other cardiovascular conditions, including hypertrophic cardiomyopathy, 17 left ventricular systolic dysfunction, 18 and aortic stenosis. 19 ML networks have also demonstrated promise identifying clinically meaningful markers of prognosis, predicting both 1-year mortality 20 and incident cardiac arrest within 24 hours.…”

Section: Introductionmentioning

confidence: 99%

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence–powered analysis of 12-lead intake electrocardiogram

Sridhar

Chen

Mayfield

et al. 2022

Cardiovascular Digital Health Journal

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Background Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that AI can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. Objective Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). Methods We studied intake ECGs from 1448 COVID-19 patients (60.5% male, 63.4±16.9 years). Records were labeled by mortality (death vs. discharge) or MACE (no events vs. arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. Results 245 (17.7%) patients died (67.3% male, 74.5±14.4 years); 352 (24.4%) experienced at least one MACE (119 arrhythmic; 107 HF; 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60±0.05 and 0.55±0.07, respectively; these were comparable to AUC values for conventional models (0.73±0.07 and 0.65±0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. Conclusion Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients’ risk of mortality or MACE. Our models’ accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

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Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation

Cited by 81 publications

References 17 publications

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence–powered analysis of 12-lead intake electrocardiogram

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