Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images

Naeha Sharif, Syed Zulqarnain Gilani, David Suter, Siobhan Reid, Pawel Szulc, Douglas Kimelman, Barret A. Monchka, Mohammad Jafari Jozani, Jonathan M. Hodgson, Marc Sim, Kun Zhu, Nicholas C. Harvey, Douglas P. Kiel, Richard L. Prince, John T. Schousboe, William D. Leslie, Joshua R. Lewis

Abstract:

Background: Lateral spine images for vertebral fracture assessment can be easily obtained on modern bone density
machines. Abdominal aortic calcification (AAC) can be scored on these images by trained imaging specialists to
assess cardiovascular disease risk. However, this process is laborious and requires careful training.

Methods: Training and testing of model performance of the convolutional neural network (CNN) algorithm for
automated AAC-24 scoring utilised 5012 lateral spine images (2 manufacturers, 4 models of bone density machines),
with trained imaging specialist AAC scores. Validation occurred in a registry-based cohort study of 8565 older men
and women with images captured as part of routine clinical practice for fracture risk assessment. Cox proportional
hazards models were used to estimate the association between machine-learning AAC (ML-AAC-24) scores with
future incident Major Adverse Cardiovascular Events (MACE) that including death, hospitalised acute myocardial
infarction or ischemic cerebrovascular disease ascertained from linked healthcare data.

Findings: The average intraclass correlation coefficient between imaging specialist and ML-AAC-24 scores for 5012
images was 0.84 (95% CI 0.83, 0.84) with classification accuracy of 80% for established AAC groups. During a
mean follow-up 4 years in the registry-based cohort, MACE outcomes were reported in 1177 people (13.7%). With
increasing ML-AAC-24 scores there was an increasing proportion of people with MACE (low 7.9%, moderate
14.5%, high 21.2%), as well as individual MACE components (all p-trend <0.001). After multivariable adjustment,
moderate and high ML-AAC-24 groups remained significantly associated with MACE (HR 1.54, 95% CI 1.31–1.80
& HR 2.06, 95% CI 1.75–2.42, respectively), compared to those with low ML-AAC-24.

Interpretation: The ML-AAC-24 scores had substantial levels of agreement with trained imaging specialists, and was
associated with a substantial gradient of risk for cardiovascular events in a real-world setting. This approach could be
readily implemented into these clinical settings to improve identification of people at high CVD risk.