- AI trained on ECG data can predict deadly heart arrhythmias up to two weeks in advance.
- Identifying 70% of at risk patients with near perfect accuracy.
- This breakthrough may revolutionize cardiac care and could soon be tested in clinical trials.
How Ai Works On Cardiology?
Ai Researchers have created an innovative approach to identifying the early warning signs of a heart attack from ultrasound images of the heart. Because heart attacks are a primary cause of death, it is important to detect them early. Diagnosing early heart attacks has been challenging because there is limited data to train on.
The researchers employed heart ultrasound images from two views and concentrated on precisely delineating the inner wall of the left ventricle using Active Polynomials. The heart wall was divided into 12 segments in order to follow its movement throughout one heartbeat, allowing minor changes that could signal an impending heart attack to be detected.
More details of the images were provided by advanced computer algorithms, in this case, one class classification (OCC) methodology. OCC requires only samples from one class in this instance, hearts with initial signs of a heart attack. The researchers pooled data from the two views of ultrasound using Multi-Modal Subspace Support Vector Data Description to map data onto a common space where the algorithm can trace a virtual boundary around patterns characteristic of early MI.
This multi modal method has detected early heart attack signs with a sensitivity of around 85% and overall accuracy of approximately 80% on a database of 260 ultrasound videos. It has successfully identified early heart attack signals from images even with sparse training data.
Computer algorithms and sophisticated image analysis indicate that physicians can soon diagnose heart attacks earlier. Timely detection results in early treatment, which can prevent further damage and save lives.
Implementation Of The Technology
If confirmed, this model based on AI could revolutionize cardiac monitoring. The algorithm might soon find its way into hospital monitoring units, allowing ongoing surveillance of susceptible patients. Moreover, developments in wearable technology can see it extend to ambulatory Holter monitors, which monitor fluctuations in blood pressure, as well as to smartwatches, providing instant risk assessment to the wearer.
In the future, the research team hopes to carry out clinical trials to test the efficacy of the AI model in real world settings. If successful, this new technology has the potential to revolutionize cardiac care, issuing early alerts and saving millions of lives by allowing for timely medical interventions.
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