Machine learning is overtaking humans in predicting death or heart attack, according to findings presented today at The International Conference on Nuclear Cardiology and Cardiac CT (ICNC).

The event is co-organised by the American Society of Nuclear Cardiology (ASNC), the European Association of Cardiovascular Imaging (EACVI) of the European Society of Cardiology (ESC), and the European Association of Nuclear Medicine (EANM).

Machine learning, the most commonly used form of artificial intelligence (AI) is increasingly used within healthcare as well as a range of other industries. Through analysing 85 variables in 950 patients with known six-year outcomes, an algorithm "learned" how imaging data interacts. It then identified patterns correlating the variables to death and heart attack with more than 90% accuracy.

“These advances are far beyond what has been done in medicine, where we need to be cautious about how we evaluate risk and outcomes,” said Dr Luis Eduardo Juarez-Orozco, of the Turku PET Centre in Finland. “We have the data but we are not using it to its full potential yet.”

Doctors use risk scores to inform their decisions about treatment. However, these can be inaccurate because they are based upon a limited number of variables make treatment decisions. In contrast, machine learning can handle large amounts of data and identify complex patterns which may not be perceptible by humans.

“Humans have a very hard time thinking further than three dimensions (a cube) or four dimensions (a cube through time),” said Juarez-Orozco. “The moment we jump into the fifth dimension we're lost. Our study shows that very high dimensional patterns are more useful than single dimensional patterns to predict outcomes in individuals and for that we need machine learning.”