By repeatedly 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 per cent accuracy.
Machine learning, the modern bedrock of artificial intelligence, is used every day, researchers said.
"These advances are far beyond what has been done in medicine, where we need to be cautious about how we evaluate risk and outcomes. We have the data but we are not using it to its full potential yet," said Luis Eduardo Juarez-Orozco, of the Turku PET Centre in Finland.
Doctors use risk scores to make treatment decisions. However, these scores are based on just a handful of variables and often have modest accuracy in individual patients.
Through repetition and adjustment, machine learning can exploit large amounts of data and identify complex patterns that may not be evident to humans.
"Humans have a very hard time thinking further than three dimensions (a cube) or four dimensions (a cube through time). The moment we jump into the fifth dimension we're lost," Juarez-Orozco said in a statement.
"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," he said.
A coronary computed tomography angiography (CCTA) scan yielded 58 pieces of data on presence of coronary plaque, vessel narrowing, and calcification.
Those with scans suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were obtained from medical records including sex, age, smoking and diabetes.
During an average six-year follow-up there were 24 heart attacks and 49 deaths from any cause. The 85 variables were entered into a machine learning algorithm called LogitBoost, which analysed them over and over again until it found the best structure to predict who had a heart attack or died.
"The algorithm progressively learns from the data and after numerous rounds of analyses, it figures out the high dimensional patterns that should be used to efficiently identify patients who have the event. The result is a score of individual risk," Juarez-Orozco said.
"Doctors already collect a lot of information about patients -- for example those with chest pain. We found that machine learning can integrate these data and accurately predict individual risk," he said.
"This should allow us to personalise treatment and ultimately lead to better outcomes for patients," he added.
(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)