Researchers from Edith Cowan University and University of Manitoba have developed a groundbreaking machine learning algorithm that can efficiently analyze bone density scans for hidden health risks. The innovative technology can detect abdominal aortic calcification, a critical marker for heart disease, strokes, and potential falls, in less than a minute. By examining routine bone scans, the algorithm provides insights into cardiovascular health that would typically go unnoticed during standard screenings. This breakthrough could significantly improve early diagnosis and preventive care for millions of older adults, particularly women who are often under-screened for cardiovascular conditions.
April 30, 2025
New machine algorithm can identify heart, fracture risks with routine bone scans
"Women are recognised as
being under-screened and under-treated for cardiovascular disease" -
Cassandra Smith, ECU Research Fellow
Australian and Canadian
researchers have developed a cutting-edge machine learning algorithm capable of
rapidly identifying heart disease and fracture risks using routine bone density
scans.
Key Points
1
Machine learning algorithm rapidly identifies heart disease risks
2
Detects abdominal aortic calcification in under one minute
3
58% of older women show moderate to high cardiovascular risk
4
Predicts fall and fracture likelihood more accurately than traditional methods
The innovation, developed by
researchers from Australia's Edith Cowan University (ECU) in conjunction with
Canada's University of Manitoba, could pave the way for more comprehensive and
earlier diagnoses during routine osteoporosis screenings, improving outcomes
for millions of older adults, Xinhua news agency reported.
The automated system analyses
vertebral fracture assessment (VFA) images to detect abdominal aortic
calcification (AAC) -- a key marker linked to heart attacks, strokes, and
falls.
Traditionally, assessing AAC
requires around five to six minutes per image by a trained expert. The new
algorithm slashes that time to under a minute for thousands of images, making
large-scale screening far more efficient, it said.
About 58 per cent of older women
undergoing routine bone scans showed moderate to high levels of AAC, many of
them unaware of the elevated cardiovascular risk, ECU research fellow Cassandra
Smith said.
"Women are recognised as
being under-screened and under-treated for cardiovascular disease," Smith
said.
"People who have AAC don't
present any symptoms, and without doing specific screening for AAC, this
prognosis would often go unnoticed. By applying this algorithm during bone
density scans, women have a much better chance of a diagnosis," Smith
added.
Further research by ECU's Marc
Sim revealed that AAC is not only a cardiovascular risk indicator but also a
strong predictor of falls and fractures. In fact, AAC outperformed traditional
fall risk factors like bone mineral density and past fall history.
"The higher the
calcification in your arteries, the higher the risk of falls and
fractures," Sim said, adding clinicians typically overlook vascular health
in fall assessments, and this algorithm changes that.
"Our analysis uncovered that
AAC was a very strong contributor to fall risks and was actually more
significant than other factors that are clinically identified as fall risk
factors."
Sim said that the new machine
algorithm, when applied to bone density scans, could give clinicians more
information about the vascular health of patients, which is an under-recognised
risk factor for falls and fractures.
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