The research aims to evaluate the effectiveness of automated abdominal aortic calcification scores in screening for atherosclerotic cardiovascular disease.
Utilized machine learning techniques to assess calcification scores on lateral spine images.
Analyzed data from the UK Biobank imaging study to identify risk factors.
Demonstrated that higher calcification scores correlate with an increased risk of atherosclerotic cardiovascular disease events.
Provided evidence for the efficacy of using automated methods for screening.
Abstract
Assessing ML-AAC24 on lateral spine images offers a new and promising screening method to identify people with higher risk of incident ASVD events.