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AI advancements in cardiac diagnostic detection

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2026-10-07 10:52 UTC → 2026-10-10 12:54 UTC · added ~~removed~~

Researchers are developing artificial intelligence models to improve the detection of various cardiac conditions through electrocardiograms (ECGs). At Wake Forest University School of Medicine, an AI model was developed to identify three specific types of heart failure—reduced ejection fraction, mildly reduced ejection fraction, and heart failure with preserved ejection fraction (HFpEF)—using standard ECGs. The model, trained on over one million ECGs, showed potential for use in wearable devices as its single-lead version performed nearly as well as the 12-lead version. Corresponding author Oguz Akbilgic, Ph.D., stated, “Our AI model can detect various types of heart dysfunction from a simple, single-lead ECG alone.” In a study published in ‘Nature’, researchers at the University of California, Berkeley, developed an algorithm to identify hidden electrical patterns to improve the detection of sudden cardiac death risk. Further advancements include an AI tool presented at the European Society of Cardiology (ESC) congress in Munich, capable of detecting heart failure and valvular disease in less than two seconds. Additionally, an international study led by Italian institutions, published in the ‘European Heart Journal – Quality of Care and Clinical Outcomes’, utilized a deep neural network trained on 84,895 ECGs and NT-proBNP tests to identify micro-electrical signals correlating with high biomarker levels. On September 26, 2026, Czech healthtech company Kardi Ai launched AI-powered long-term cardiac monitoring in India. Following this, researchers at NYU Langone Health developed an AI model to detect heart transplant rejection. By combining ECG readings with common blood tests, the model demonstrated high accuracy, correctly identifying 94 percent of patients not experiencing rejection in a test group, potentially reducing the need for invasive biopsies. Further developments in cardiology include the presentation of ECG-CLIP at the ESC congress by researchers from Imperial College London.

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