Researchers from Cedars-Sinai Medical Center in the US used machine learning to assess the risk of myocardial infarction or heart attack and cardiac death in the subjects.
They then compared the predictions with the actual experiences of the subjects over fifteen years.
Machine learning is an application of AI which gives the computers the ability to automatically learn and improve from experience.
Subjects answered a questionnaire to identify cardiovascular risk factors and to describe their diets, exercise and marital status.
The final study consisted of 1,912 subjects, fifteen years after they were first studied.
As many as 76 subjects presented an event of myocardial infarction or cardiac death during this follow-up time.
The subjects’ predicted machine learning scores aligned accurately with the actual distribution of observed events.
The atherosclerotic cardiovascular disease risk score, the standard clinical risk assessment used by cardiologists, overestimated the risk of events in the higher risk categories, the researchers said.
In unadjusted analysis, high predicted machine learning risk was significantly associated with a higher risk of a cardiac event.
“Our study showed that machine learning integration of clinical risk factors and imaging measures can accurately personalise the patient’s risk of suffering an adverse event such as heart attack or cardiac death,” the researchers said.
“While machine learning models are sometimes regarded as black boxes, we have also tried to demystify machine learning. When applied after the scan, such individualised predictions can help guide recommendations for the patient, to decrease their risk of suffering an adverse cardiac event,” they said.