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Researchers from Edith Cowan University (ECU) alongside the University of Manitoba have developed an innovative automated machine learning program that can identify potential cardiovascular risks as well as the likelihood of falls and fractures through bone density scans performed during routine clinical evaluations.
This algorithm is particularly effective when applied to vertebral fracture assessment (VFA) images, which are commonly captured in older women undergoing bone density testing as part of osteoporosis treatment plans. The assessment focuses on the presence and severity of abdominal aortic calcification (AAC).
Notably, the algorithm significantly reduces the time required for screening AAC, completing the task in under a minute for thousands of images. In contrast, a seasoned professional typically takes five to six minutes to evaluate the AAC score from a single image.
ECU research fellow Dr. Cassandra Smith’s investigation revealed that 58% of older individuals screened during routine bone density tests exhibited moderate to high levels of AAC. Alarmingly, one in four participants entered the clinic unaware of their elevated AAC levels, putting them at an increased risk of serious cardiovascular events such as heart attacks and strokes.
“Women often face under-screening and under-treatment concerning cardiovascular disease,” Dr. Smith remarked. “This research demonstrates that we can utilize commonly available, low-radiation bone density machines to identify women who are at high risk of cardiovascular conditions, which facilitates early intervention.”
According to Dr. Smith, individuals with AAC typically do not exhibit symptoms. Without targeted screening for AAC, such risks often remain unrecognized. Implementing this algorithm during bone density assessments greatly enhances the chances of proper diagnosis for women.
In parallel findings, ECU senior research fellow Dr. Marc Sim observed that patients with moderate to high AAC scores were also at a heightened risk of hospitalization due to falls, as well as sustaining fractures when compared to those with lower AAC scores.
“Increased calcification in arteries correlates with a greater risk of falls and fractures,” Dr. Sim explained. “Traditional risk factors, such as a history of falls within the past year and bone mineral density, serve as reliable indicators. Additionally, certain medications may elevate fall risks; however, vascular health is seldom considered when assessing fall and fracture risks.”
Dr. Sim’s analysis indicated that AAC significantly contributes to the risk of falls, surpassing other clinically recognized factors. He emphasized that employing this new algorithm in bone density scans equips clinicians with valuable insights into patients’ vascular health, an often-overlooked aspect in the evaluation of fall and fracture risk.
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