Sydney, April 30
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.
— IANS
Reader Comments
This is revolutionary for Indian healthcare! Our aging population suffers from both osteoporosis and heart disease. If this tech comes to India, it could save countless lives through early detection. Hope our hospitals adopt it soon 🤞
Interesting research but I wonder about cost-effectiveness for Indian hospitals. Our doctors are already overburdened - will this actually help or just add more data to analyze? The tech sounds promising but implementation is key.
As someone whose mother suffered multiple fractures, this makes me emotional. In India we often ignore bone health until it's too late. Combining heart and fracture risk detection is brilliant! Hope Indian researchers collaborate on similar projects.
Good research but will it work for Indian bodies? Our physiology and disease patterns are different from Western populations. Hope they validate it for diverse ethnic groups before implementation.
This is why we need more investment in medical AI! Indian women especially neglect heart health until problems appear. My nani always said "prevention is better than cure" - this tech proves that ancient wisdom right 😊
While impressive, I'm concerned about data privacy. If Indian hospitals adopt this, we need strong safeguards for patient data. Our digital health mission must balance innovation with security.
Here are 6 diverse Indian perspective comments for the article: We welcome thoughtful discussions from our readers. Please keep comments respectful and on-topic.