  Abdominal Aortic Calcification (AAC) is a known marker of asymptomatic
Atherosclerotic Cardiovascular Diseases (ASCVDs). AAC can be observed on
Vertebral Fracture Assessment (VFA) scans acquired using Dual-Energy X-ray
Absorptiometry (DXA) machines. Thus, the automatic quantification of AAC on VFA
DXA scans may be used to screen for CVD risks, allowing early interventions. In
this research, we formulate the quantification of AAC as an ordinal regression
problem. We propose a novel Supervised Contrastive Ordinal Loss (SCOL) by
incorporating a label-dependent distance metric with existing supervised
contrastive loss to leverage the ordinal information inherent in discrete AAC
regression labels. We develop a Dual-encoder Contrastive Ordinal Learning
(DCOL) framework that learns the contrastive ordinal representation at global
and local levels to improve the feature separability and class diversity in
latent space among the AAC-24 genera. We evaluate the performance of the
proposed framework using two clinical VFA DXA scan datasets and compare our
work with state-of-the-art methods. Furthermore, for predicted AAC scores, we
provide a clinical analysis to predict the future risk of a Major Acute
Cardiovascular Event (MACE). Our results demonstrate that this learning
enhances inter-class separability and strengthens intra-class consistency,
which results in predicting the high-risk AAC classes with high sensitivity and
high accuracy.
