Innovative AI-Powered ECG Tool Shows Promise for Early Heart Failure Detection in Kenya

A recent study highlights the potential of an artificial intelligence (AI)-driven electrocardiogram (ECG) algorithm to identify early signs of heart failure in individuals seeking medical care in Kenya. Presented at the Heart Failure 2025 conference, researchers revealed that the AI-based tool demonstrated high accuracy in detecting left ventricular systolic dysfunction (LVSD), a precursor to heart failure, providing a promising scalable screening option in resource-limited settings.

Heart failure remains a prevalent health issue across Sub-Saharan Africa, affecting younger populations with worse health outcomes compared to high-income countries. Early detection of LVSD, which involves impaired function of the heart's left ventricle, is crucial for timely intervention. However, standard diagnosis via echocardiography is often inaccessible in regions with limited healthcare infrastructure.

To address this challenge, researchers led by Dr. Ambarish Pandey from the University of Texas Southwestern Medical Center conducted a multicenter, prospective screening study involving nearly 6,000 adults at eight healthcare facilities in Kenya. Participants underwent standard 12-lead ECG tests, and their risk of LVSD was evaluated using a validated AI algorithm developed by Medical AI Co. in Seoul, Korea. The AI tool analyzed ECG data to predict the likelihood of LVSD, with a specific threshold to classify individuals as high risk.

The study found an overall LVSD prevalence of 18.3%, which was significantly higher among participants with elevated Framingham Risk Scores (>10%) and those with known cardiovascular disease. In a subset of 1,444 participants who underwent both AI-based assessment and echocardiography, the AI tool showed exceptional diagnostic performance, with a sensitivity of 95.6%, specificity of 79.4%, and a negative predictive value of 99.1%. These results suggest that AI-enabled ECG analysis could serve as a low-cost, scalable screening method to identify individuals at risk of developing heart failure.

Dr. Bernard Samia, President of the Kenya Cardiac Society, emphasized the broader implications: "Our findings indicate that AI-ECG algorithms can be valuable in detecting heart disease in resource-poor settings, potentially enabling earlier intervention and improved outcomes." Dr. Pandey highlighted the importance of such technology: "Identifying nearly one in five individuals with LVSD demonstrates the significant burden and the critical need for accessible screening tools."

Looking ahead, researchers plan to expand screening efforts across multiple African countries and explore how early identification impacts the use of evidence-based therapies. This advancement underscores the critical role of AI in transforming cardiovascular healthcare in underserved regions, ultimately aiming to reduce the global burden of heart failure.

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