Chennai: Artificial Intelligence can now be deployed to predict medical conditions like a heart attack or stroke, it is learnt.
A new study presented at the annual meeting of the Radiological Society of North America (RSNA), has found that automated deep learning analysis of abdominal CT images produces a more precise measurement of body composition and predicts major cardiovascular events, such as heart attack and stroke, better than overall weight or body mass index (BMI).
According to Kirti Magudia, an abdominal imaging and ultrasound fellow at the University of California San Francisco, “Established cardiovascular risk models rely on factors like weight and BMI that are crude surrogates of body composition. It’s well established that people with the same BMI can have markedly different proportions of muscle and fat. These differences are important for a variety of health outcomes”.
Unlike BMI, which is based on height and weight, a single axial CT slice of the abdomen visualises the volume of subcutaneous fat area, visceral fat area and skeletal muscle area. However, manually measuring these individual areas is time intensive and costly.
Statistical analysis demonstrated that visceral fat area was independently associated with future heart attack and stroke. BMI was not associated with heart attack or stroke.
“The group of patients with the highest proportion of visceral fat area were more likely to have a heart attack, even when adjusted for known cardiovascular risk factors”, said Magudia, while the group of patients with the lowest amount of visceral fat area were protected against stroke in the years following the abdominal CT exam.
“These results demonstrate that precise measures of body muscle and fat compartments achieved through CT outperform traditional biomarkers for predicting risk for cardiovascular outcomes”, she added.