Several studies explore the prediction of heart attacks and strokes using echocardiography (echo) data combined with artificial intelligence (AI) techniques. A key approach involves using non-invasive imaging, such as echocardiograms, to analyze heart structure and function, combined with electrocardiography (ECG) data to detect atrial dysfunction. This can help identify conditions like atrial cardiomyopathy, which is linked to higher risks of atrial fibrillation and cardioembolic strokes. For example, a recent study reviewed how left atrial dysfunction, visible on an echocardiogram, can predict stroke risks, especially in patients at high risk for atrial fibrillation [oai_citation:2,JCM | Free Full-Text | Echocardiography and Electrocardiography in Detecting Atrial Cardiomyopathy: A Promising Path to Predicting Cardioembolic Strokes and Atrial Fibrillation](https://www.mdpi.com/2077-0383/12/23/7315).
Another study emphasized AI's role in preventive cardiology, focusing on predicting heart attack risks. The research demonstrated that combining patient data, such as heart rate, BMI, age, and cholesterol levels, with AI models like logistic regression can offer moderately accurate early predictions of heart attack risks. These models help target patients who need further diagnostics [oai_citation:1,Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine](https://www.mdpi.com/2079-9292/13/2/272). Both studies highlight the growing role of AI in leveraging patient data and echo results to enhance cardiovascular risk prediction and prevention strategies.
Understanding ECG