Researchers from Edith Cowan University (ECU) and the University of Manitoba have developed an automatic program that may discover cardiovascular problems and fall risks from routine bone density scans.
This could make it considerably easier to detect serious health issues before they develop into life-threatening.
The algorithm, developed by ECU research fellow Dr. Cassandra Smith and senior research fellow Dr. Marc Sim, works by analyzing vertebral fracture assessment (VFA) images taken during standard bone density tests, which are sometimes a part of treatment plans for osteoporosis.
By assessing the presence and extent of abdominal aortic calcification (AAC) in these scans, this system can quickly flag patients prone to heart attack, stroke, and dangerous falls.
What’s truly impressive is the speed at which the algorithm works. While an experienced human reader might take five to 6 minutes to calculate an AAC rating from a single scan, the machine learning program can predict scores for hundreds of images in lower than a minute.
That level of efficiency might be a major profit for healthcare systems trying to screen large populations for hidden health risks.
The need for such screening is clear. In the research, Dr. Smith found that a staggering 58% of older individuals who underwent routine bone density scans had moderate to high levels of AAC.
Even more concerning, one in 4 of those patients were completely unaware of their elevated risk.
“Women are recognized as being under-screened and under-treated for heart problems,” Dr. Smith noted. “This study shows that we are able to use widely available, low-radiation bone density machines to discover women at high risk of heart problems, which might allow them to hunt treatment.”
But the algorithm’s predictive power doesn’t stop at heart health. Using the identical program, Dr. Sim discovered that patients with moderate to high AAC scores were also at greater risk of fall-related hospitalizations and fractures in comparison with those with low scores.
“The higher the calcification in your arteries, the upper the chance of falls and fractures,” Dr. Sim explained. While traditional fall risk aspects like previous falls and low bone density are well-known, vascular health isn’t considered.
“Our evaluation uncovered that AAC was a really strong contributor to falls risks and was actually more significant than other aspects which can be clinically identified as falls risk aspects.”
As with any latest technology, there are inquiries to be answered and challenges to beat before this type of AI-assisted screening becomes standard practice.
First and foremost, the algorithm will have to be validated in larger, more diverse patient populations and integrated seamlessly into existing clinical workflows.
However, if those challenges will be met, a straightforward bone scan – something hundreds of thousands of older adults already undergo often – could develop into an early warning system for a few of the most typical and devastating health problems we face.
