● Covered by 1 source · 1 reportMedium impact1 positive
AI Tool Detects Heart Disease from ECGs in Under Two Seconds
Key points
- AI tool analyzes ECGs to detect heart failure and heart valve disease.
- Identifies conditions in less than two seconds.
- Trial showed 81% accuracy for heart failure, 90% for valve disease.
- Could fast-track high-risk patients for further diagnosis.
New AI for Heart Disease Detection
A team of doctors has created an AI tool capable of detecting heart failure and heart valve disease from routine electrocardiogram (ECG) readings. This technology processes ECG data in under two seconds, extracting information beyond what is typically visible to the human eye.
Addressing Diagnostic Gaps
Traditional ECGs are effective for diagnosing heart attacks and abnormal rhythms but cannot detect heart disease, which typically requires an echocardiogram. Patients often face months-long waits for echocardiograms. This AI tool aims to bridge that gap by providing early indications from readily available ECGs.
Trial Results and Potential Impact
In a trial involving 67,000 patients in the US, the AI tool identified up to 81% of individuals with heart failure and up to 90% of those with heart valve disease. This breakthrough was presented at the European Society of Cardiology annual congress.
The development is significant because approximately one billion ECGs are performed globally each year. While the AI cannot definitively diagnose conditions, it can strongly indicate their presence, allowing patients deemed high-risk to be fast-tracked for further diagnostic tests like echocardiograms, potentially saving lives through earlier treatment.
✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →
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Reporting from
Doctors have developed an AI tool that can identify signs of heart failure and heart valve disease from standard electrocardiogram (ECG) results in under two seconds. This development could accelerate early diagnosis for these common heart conditions, potentially reducing patient wait times for further diagnostic tests like echocardiograms.