arXiv:2601.00014v2 Announce Type: replace-cross Abstract: Heart failure (HF) affects 11.8% of adults aged 65 and older, reducing quality of life and longevity. Preventing HF can reduce morbidity and mortality. We hypothesized that artificial intelligence (AI) applied to 24-hour single-lead electrocardiogram (ECG) data could predict the risk of HF within five years. To research this, the Technion-Leumit Holter ECG (TLHE) dataset, including 69,663 recordings from 47,729 patients, collected over 20 years was used. Our deep learning model, DeepHHF, trained on 24-hour ECG recordings, achieved an ar
Advances in AI, particularly deep learning, combined with increasing access to large medical datasets like the Technion-Leumit Holter ECG (TLHE), are making such predictive models feasible and robust.
Early and accurate prediction of heart failure risk using non-invasive, widely available methods like ECGs can significantly improve patient outcomes, reduce healthcare costs, and extend healthy lifespans.
The capability to predict heart failure risk years in advance using AI-analyzed ECG data shifts medical practice towards proactive intervention and preventive care for a major chronic disease.
- · AI healthcare providers
- · Cardiovascular diagnostics companies
- · Patients at risk of heart failure
- · Digital health platforms
- · Traditional diagnostic methods
- · Hospitals burdened by late-stage HF admissions
Widespread adoption of AI-powered ECG analysis for preventive cardiovascular health screening.
Increased demand for, and investment in, large, diverse medical datasets and explainable AI in healthcare.
Potential for AI to become a standard tool for population-level health risk assessment across various chronic conditions, influencing public health policy and insurance models.
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