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New AI Model Revolutionize Heart Attack Diagnosis for Doctors

Chest pain is a common reason for emergency department visits globally, and it’s a challenging duty for doctors to differentiate between heart attack-related pain and less severe causes.

Researchers from the University of Edinburgh in the UK have created a unique algorithm named CoDE-ACS using artificial intelligence (AI) that could assist doctors in diagnosing heart attacks accurately and quickly.

When compared to testing methods now in use, the algorithm demonstrated encouraging results with an accuracy of 99.6% during the diagnosis of a heart attack in twice as many individuals.

With this development, fewer people would need to be admitted to the hospital, and those who could be safely discharged would be found more rapidly. The study outlining these conclusions was released in the journal Nature Medicine.

According to Prof. Nicholas Mills, who led the research, early diagnosis and treatment are crucial for saving the lives of patients with acute chest pain caused by a heart attack. However, diagnosing the condition is not always straightforward due to various factors causing similar symptoms.

Prof. Mills emphasized how data and AI may be used to assist clinical judgements, enhancing patient care and efficiency in emergency wards.

The algorithm approach also aids medical professionals in distinguishing between unusual troponin levels (defined as protein released into blood stream caused by heart attack) brought on by a heart attack and those brought on by other health conditions.

The model was created by using Data of 10,038 individuals in Scotland who were hospitalized with suspected cardiac attacks. To determine the likelihood of a heart attack, the system uses patient data such as ECG results, troponin levels, age, sex, and medical history.

In Scotland, clinical trials are being conducted to see if CoDE-ACS can assist doctors in easing the pressure on packed emergency rooms.

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