New machine algorithm can identify heart, fracture risks wit
New machine algorithm can identify heart, fracture risks with routine bone scans
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.
SYDNEY — 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.
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.
