A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction

Butler, Liam; Karabayir, Ibrahim; Kitzman, Dalane W.; Alonso, Alvaro; Tison, Geoffrey H.; Chen, Lin Yee; Chang, Patricia P.; Clifford, Gari; Soliman, Elsayed Z.; Akbilgic, Oguz

doi:10.1016/j.cvdhj.2023.11.003

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Butler L. et al developed AI models based on ECG data that predict the risk of Heart Failure (HF) within a 10-year period with accuracy comparable to or better than current HF risk calculators and traditional ECG methods. This research has several objectives, including the potential use of these AI models to enable cost-effective and remote monitoring of at-risk groups, thereby facilitating prompt interventions and enhancing clinical decision-making [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Patrascanu,

Tutunaru,

Musat

et al. 2024

JPM

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Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

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Section: Resultsmentioning

confidence: 99%

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Patrascanu,

Tutunaru,

Musat

et al. 2024

JPM

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“…There is also potential in developing single-lead ECG-based models for remote monitoring using smart wearables for pregnancies, especially among high risk women. We have previously shown that single-lead models perform well for the prediction and detection of heart failure ( 19 , 46 ) and fatal coronary heart disease ( 50 ) using solely Lead I of a 12-Lead ECG, which is mimicked by smart watches and other smart devices with ECG monitoring capabilities. A similar approach can be taken for preeclampsia risk assessment.…”

Section: Discussionmentioning

confidence: 99%

“…This final model was then tested on the 20% hold-out data. A modified ResNet CNN, reported in Akbilgic et al was used to predict the incidence of preeclampsia ( 17 – 19 ). The CNN algorithm uses one-dimensional (1D) ECG signal with 12 channels (each ECG lead being one channel) as an input.…”

Section: Methodsmentioning

confidence: 99%

AI-based preeclampsia detection and prediction with electrocardiogram data

Butler,

Gunturkun,

Chinthala

et al. 2024

Front. Cardiovasc. Med.

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IntroductionMore than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.MethodsTen-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.ResultsThe UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00).DiscussionWe conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.

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“…Analysis of 12-lead ECG with CNN (Convolutional Neural Network) was able to detect Mitral valve prolapse (MVP), which is at high risk of ventricular arrhythmia, death, and or fibrosis (Tison et al, 2023). There are also generalized ECG-AI studies to predict the likelihood of experiencing heart failure in the upcoming decade (Butler et al, 2023). ECG is also used to detect arrhythmias in cats.…”

Section: Introductionmentioning

confidence: 99%

Application of Decision Tree Method in ECG Signal Classification For Heart Disorder Detection

Banjarnahor,

Sinaga,

Sitorus

et al. 2024

SinkrOn

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Cardiovascular Disease (CVD) is a group of diseases that affect the heart and blood vessels, and it is the leading cause of death globally. In Indonesia, Coronary Heart Disease (CHD) is one of the most prevalent CVDs. However, due to the high cost of drugs, lengthy treatment duration, and various supporting examinations required, treating CHD can be very expensive. An obstacle to treating heart disease in Indonesia is the insufficient number of cardiologists and experts experienced in interventional cardiology. Along with technological developments, the computer science community is encouraged to contribute to the medical field. For instance, using an electrocardiogram (ECG) can help prevent and minimize problems arising from heart disease. An Electrocardiogram (ECG) is a medical test that measures and records the heart’s electrical activity using a machine that detects electrical impulses. The use of Artificial Intelligence (AI) in ECG is rapidly increasing and has shown to have great potential in improving the diagnosis and treatment of cardiac patients. AI has become a valuable tool in helping doctors diagnose, predict risk, and manage heart disease with greater accuracy, speed, and precision. One of the machine learning methods used in this research is the decision tree method, which is often employed to make decisions. The decision tree method exhibited promising results, with an accuracy rate of 99% in identifying heart defects at an early stage. This method has significant potential to assist doctors in diagnosing heart defects at an early stage with high accuracy.

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A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction

Cited by 6 publications

References 29 publications

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

AI-based preeclampsia detection and prediction with electrocardiogram data

Application of Decision Tree Method in ECG Signal Classification For Heart Disorder Detection

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