Heart Attack Diagnosis Enhanced: Doctors on the Brink of Leveraging New AI Model

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The researchers from the UK have created a unique algorithm that utilizes artificial intelligence (AI) to assist doctors in the quick and accurate diagnosis of heart attacks.

As per the University of Edinburgh researchers, the recently developed algorithm, CoDE-ACS, was capable of accurately ruling out a heart attack in over twice the number of patients as compared to current testing methods, with a precision of 99.6%.

The CoDE-ACS algorithm could also be very useful in decreasing hospital admissions and quickly determining which patients are safe to be discharged. These findings have been published in the journal Nature Medicine.

Prof. Nicholas Mills, who led the research, stated that early diagnosis and treatment are crucial to save lives of patients with acute chest pain due to a heart attack.

“Regrettably, numerous ailments can trigger these ordinary symptoms, and the diagnosis is not always simple.”

Prof. Nicholas Mills, who led the research, noted that using data and artificial intelligence to support clinical decisions has huge potential to enhance patient care and increase efficiency in emergency departments.

To add clarity and originality, the sentence can be rewritten as: “Moreover, the CoDE-ACS algorithm has the potential to help healthcare professionals distinguish between patients whose abnormal troponin levels are due to a heart attack and those whose elevated levels are due to other medical conditions.”

Prof. Sir Nilesh Samani, the medical director of the British Heart Foundation, stated that chest pain is a frequent cause of people seeking medical attention at emergency departments.

Prof. Sir Nilesh Samani, medical director of the British Heart Foundation, added that doctors worldwide face the daily challenge of distinguishing patients whose chest pain is caused by a heart attack from those whose pain is due to a less serious condition.

The CoDE-ACS algorithm was developed by analyzing data from 10,038 patients in Scotland who had been admitted to hospitals with suspected heart attacks.

The algorithm, CoDE-ACS, utilizes commonly gathered patient data like age, sex, medical history, ECG results, and troponin levels to estimate the likelihood of a patient experiencing a heart attack.

The algorithm generates a probability score ranging from 0 to 100 for each patient.

Clinical trials are currently being conducted in Scotland to evaluate whether the CoDE-ACS algorithm can assist doctors in alleviating the burden on overcrowded emergency departments.

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