Discover how the AIRE AI-ECG model predicts heart disease and mortality risk with greater accuracy than traditional methods—your heart’s new guardian.
- The AI-ECG risk estimation (AIRE) model accurately predicts mortality and cardiovascular disease risk, outperforming traditional methods
- AIRE’s predictions remain accurate using single-lead ECG data, enabling remote heart health monitoring from consumer devices
- The model bridges the gap in early diagnosis for patients without a CVD history, offering actionable insights for clinical practice
A novel artificial intelligence (AI)-enhanced electrocardiography (ECG) model that can use patients’ medical histories and imaging results to predict mortality and cardiovascular disease (CVD) risk accurately, has been created and validated in a recent study published in the journal The Lancet (1✔ ✔Trusted Source
Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study
).
While not the first attempt to apply AI in disease and mortality prediction, this implementation overcomes the limitations of earlier models in terms of timing, biological plausibility, and explainability, allowing it to create predictions that can enable actionable insights in clinical practice.
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AI-ECG Risk Estimation (AIRE): The Novel AI-based ECG Model
The study found that the novel model (known as ‘AIRE’- AI-ECG risk estimation) can reliably predict all-cause mortality, ventricular arrhythmia, atherosclerotic CVD, and heart failure risk. It outperformed traditional AI models in terms of short- and long-term risk estimations, giving physicians insights for short-term, single-point diagnostic predictions while also recommending long-term, progressive therapies for the patient’s remaining pharmaceutical support.
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What are Electrocardiograms?
Electrocardiograms (ECGs) are non-invasive graphical assessments of cardiovascular electrical activity. The procedure includes placing external electrodes strategically on patients’ chests, arms, and legs to provide clinicians with visual representations of heart electrical signals and rhythms.
ECGs have been used in cardiovascular examinations for over a century, and their methodology has remained mostly unaltered.
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Using AI to Predict Patients’ Heart Disease and Mortality Risk
Recent developments in computer processing capabilities, as well as the introduction of next-generation predictive machine learning (ML) models, have generated interest in the research community.
Since 2020, a few studies have attempted to use ECG-data-trained artificial intelligence (AI) models to predict patients’ CVD and mortality risk, highlighting model performance: in almost every implementation of AI in disease/mortality risk prediction, AI models achieve diagnostic and predictive performance comparable to, or exceeding human expert predictions.
Thus, AI models have the potential to reduce patient burdens on clinicians (geographically defined number of individuals per number of doctors), notably in rural and impoverished areas, while also increasing diagnostic speed and lowering patient costs.
Unfortunately, while being clinically validated for safety and efficacy, AI-enhanced ECG models are rarely used in real-world ECG applications.
From a research standpoint, while accurate, early AI implementations provided insufficient explanations of model performance (a computational ‘black box’) and biological plausibility, prompting physicians to be skeptical of model predictions.
AI-ECG Risk Estimation (AIRE) Platform Predicts Mortality Risk Accurately
In the current study, researchers create, train, and evaluate eight innovative AI-ECG risk estimation (AIRE) models (together referred to as the ‘AIRE platform’) to predict mortality risk (all-cause and cardiovascular) without the constraints of earlier AI implementations.
Notably, AIRE was capable of properly predicting heart failure events in people without a personal or family history of CVD, which is very important given that conventional diagnoses in these patients are frequently delayed.
Encouragingly, AIRE results remained solid even when presented with single-lead ECG data (from consumer devices; clinical ECG equipment have 8-12 leads), demonstrating the platform’s potential for stay-at-home CVD risk monitoring.
The model had sufficient biological plausibility, explaining that surrogate pulmonary pressure measures and ventricular diameter correlated inversely with predicted survival, whereas left ventricular ejection fraction (LVEF) correlated directly.
AIRE Platform: AI-enhanced ECG Examination Tool
The current work creates and evaluates the AIRE platform, which is the most clinically applicable AI-enhanced ECG examination tool available today. According to the study’s findings, the platform surpasses traditional human-based predictions and similar older-generation AI models in terms of predicted accuracy, with the latter not requiring demographic or medical history data.
Notably, the model remained strong even when given single-lead data from consumer devices, demonstrating AIRE’s promise for remote patient monitoring, particularly among patients with no medical CVD history or in distant places without adequate clinical assistance.
Reference:
- Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study – (https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00172-9/fulltext)
Source-Medindia
