The authors of this AI program expect that it would drastically reduce unnecessary emergency room visits while also saving lives through timely diagnosis.
Artificial intelligence and health experts have developed a new tool to help clinicians diagnose heart attacks swiftly and correctly. The AI tool’s designers expect that it would drastically reduce unnecessary admissions to already overcrowded emergency rooms and save lives through rapid diagnosis, benefiting both heart patients and hospitals.
Researchers tested a new AI tool called ‘CoDE-ACS’ on over 10,286 people in a trial. When compared to current testing approaches, the CoDE-ACS was found to rule out the possibility of a heart attack in twice as many people.
With a reported accuracy of 99.6%, the CoDE-ACS was said to be the most accurate heart attack detection tool.
The CoDE-ACS is currently undergoing clinical testing in Scotland, thanks to Wellcome Leap, a firm founded to accelerate healthcare discovery and innovation. The gadget is being tested by Wellcome Leap to see if it might help reduce pressure on overcrowded hospital emergency departments.
Nicholas Mills, a professor of cardiology at the University of Edinburgh’s Center for Cardiovascular Science and the study’s principal investigator, stated:
Early detection and treatment of persons experiencing acute chest pain as a result of a heart attack saves lives. Unfortunately, these extensive symptoms might be caused by a variety of conditions, and a prompt diagnosis is not always attainable.
He also said that it has a huge chance of making care for patients and speed in our busy emergency rooms much better. Harnessing data and artificial intelligence to back clinical decisions.
The measurement of troponin levels in the blood is currently the gold standard for detecting whether a patient is having a heart attack. The protein troponin technique, on the other hand, differs from person to person because age, gender, and other health issues influence the results.
Women who have the protein troponin test are apparently 50% more likely to obtain an incorrect diagnosis at first. Because an incorrect diagnosis of a heart attack increases the likelihood of death by 70%, this considerably increases risk levels.
The CoDE-ACS, which was designed and trained using data from over 10,000 cardiac patients, can prevent this because it uses a range of signs to assess whether a person is experiencing a heart attack, including age, gender, ECG test results, medical history, and troponin levels.
When compared to current methods, CoDE-ACS can rule a heart attack in or out with greater accuracy. According to Professor Sir Nilesh Samani, MD, of the British Heart Foundation, it could be transformational for emergency departments, cutting down on the time needed to make a diagnosis, and much better for patients.
