SYDNEY, April 30 (Xinhua) — 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, Australia’s Edith Cowan University (ECU) has revealed.
The innovation, which was developed 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, said researchers at ECU in Western Australia in a news release on Tuesday.
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…