Mining of body composition from abdominal CT using AI can predict risk of CVD, fracture and death
USA: Computed tomography (CT)-based body composition measures derived from fully automated artificial intelligence (AI) tools can help predict the risk of cardiovascular disease, death, and even bone fractures, researchers state in a recent study published in Radiology.
Abdominal CT exams are a cornerstone of general and abdominal subspecialty radiology practice and have body composition data, which mostly gets underutilized in routine clinical practice. Besides population-based prediction, CT-based AI body composition tools measuring abdominal fat, muscle attenuation, and abdominal aortic calcium give an exciting chance for more personalized cardiometabolic opportunistic screening, risk stratification, and prediction of adverse clinical outcomes.
Matthew H. Lee, the University of Wisconsin, Madison, and colleagues explored population and sex-specific thresholds for abdominal aortic calcium measures and muscle and abdominal fat at abdominal CT for predicting the risk of adverse cardiovascular events, death, and fractures in a retrospective single-center study.
The researchers applied fully automated algorithms for determining abdominal fat (L3 level), skeletal muscle (L3 level), and abdominal aortic calcium to non-contrast abdominal CT scans obtained from asymptomatic adults screened from 2004 to 2016. Longitudinal follow-up recorded adverse cardiovascular events (cerebrovascular events, heart failure, and myocardial infarction), subsequent death, and fragility fractures. To derive thresholds for body composition measures for achieving optimal ROC curve performance and high specificity (90%) for 10-year risks, receiver operating characteristic (ROC) curve analysis was performed.
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