Doctors could soon use AI to diagnose HEART ATTACKS

Doctors could soon use AI to diagnose HEART ATTACKS: Algorithm can rule out cardiac incidents with 99.6% accuracy

  • Scientists have developed an algorithm which could reduce pressure on A&E
  • It rules out a heart attack in more than double the patient than current methods 

Heart attacks could soon be diagnosed with better speed and accuracy than ever before thanks to a new AI tool.

Researchers have developed an algorithm which they say could reduce pressure on A&E and reassure patients suffering from chest pain.

A new study suggests that compared to current testing methods, their algorithm was able to rule out a heart attack in more than double the number of patients with an accuracy of 99.6 per cent.

The team, from the University of Edinburgh, said this ability to quickly rule out a heart attack could greatly reduce hospital admissions and rapidly identify patients that are safe to go home.

The current gold standard for diagnosing a heart attack involves measuring levels of the protein troponin in the blood.

Researchers have developed an algorithm which they say could reduce pressure on A&E and reassure patients suffering from chest pain (stock image)


Figures suggest there are 200,000 hospital visits because of heart attacks in the UK each year, while there are around 800,000 annually in the US.

A heart attack, known medically as a myocardial infarction, occurs when the supply of blood to the heart is suddenly blocked. 

Symptoms include chest pain, shortness of breath, and feeling weak and anxious.

Heart attacks are commonly caused by coronary heart disease, which can be brought on by smoking, high blood pressure and diabetes.

Treatment is usually medication to dissolve blots clots or surgery to remove the blockage.

Reduce your risk by not smoking, exercising regularly and drinking in moderation.

Heart attacks are different to a cardiac arrest, which occurs when the heart suddenly stops pumping blood around the body, usually due to a problem with electrical signals in the organ. 

Source: NHS Choices

But the same threshold is used for every patient, meaning factors such as age, sex and other health problems which affect troponin levels are not considered – affecting how accurate heart attack diagnoses are.

Previous research has shown that women are 50 per cent more likely to get a wrong initial diagnosis, and people who are misdiagnosed have a 70 per cent higher risk of dying after 30 days.

The team said their new algorithm, called CoDE-ACS, is an opportunity to prevent this.

It was developed using data from 10,038 patients in Scotland who had arrived at hospital with a suspected heart attack.

It uses routinely-collected patient information, such as age, sex, ECG findings and medical history, as well as troponin levels, to predict the probability that an individual has had a heart attack.

The result is presented as a probability score from 0 to 100 for each patient.

Professor Nicholas Mills, who led the research, said: ‘For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives.

‘Unfortunately, many conditions cause these common symptoms, and the diagnosis is not always straightforward.

‘Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy Emergency Departments.’

Professor Sir Nilesh Samani, Medical Director of the British Heart Foundation, who funded the research, said: ‘Chest pain is one of the most common reasons that people present to Emergency Departments.

‘Every day, doctors around the world face the challenge of separating patients whose pain is due to a heart attack from those whose pain is due to something less serious.

Figures suggest there are 200,000 hospital visits because of heart attacks in the UK each year, while there are around 800,000 annually in the US (stock image)

‘CoDE-ACS, developed using cutting edge data science and AI, has the potential to rule-in or rule-out a heart attack more accurately than current approaches.

‘It could be transformational for Emergency Departments, shortening the time needed to make a diagnosis, and much better for patients.’

Figures show there are around 100,000 hospital admissions each year in the UK due to heart attacks – the equivalent of one every five minutes.

Clinical trials are now underway in Scotland to assess whether the AI tool can help doctors reduce pressure on overcrowded emergency departments.

The findings were published in the journal Nature Medicine.


In a person’s lifetime, a human heart can contract billions of times.

The heart is still a muscle, much like the bicep or the hamstring, but the heart never tires. 

The reason for this rather crucial detail of anatomy keeps us alive, as without a pumping heart death will shortly follow. 

Hearts, although muscles, are made up of different fibres than their counterparts.

This type of fibre, known as cardiac tissue, only exists in the heart and nowhere else in the human body. 

Skeletal muscle tires quickly, and can switch from aerobic respiration to anaerobic respiration – producing lactic acid which causes cramp.

If this was to happen in the heart it would cause a heart attack.

To avoid this happening and to allow for constant use without fatigue, the cardiac tissue has a different arrangement.

Cardiac tissue has far more mitochondria which produces a huge amount more energy in the form of a chemical called Adenosine Triphosphate (ATP).

Mitochondria are small organelles within cells which are considered to be the powerhouse of the cell and convert glucose into energy inside the organelle. 

Having more of these means the heart as an organ will never run out of energy under normal circumstances. 

The reason this arrangement does not occur in all muscle sis the energy requirements would be enormous, and unsustainable. 

The human body would simply demand more energy than it can create. 

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