AI Algorithm Detects Early Signs of Heart Disease From Routine Bone Scans

Heart disease remains one of the world's most prolific killers, often operating in silence until a catastrophic event occurs. For decades, the medical community has sought a cost-effective, non-invasive way to identify at-risk patients before they suffer a heart attack or stroke. Now, a breakthrough from an Australian research team is turning an entirely different medical procedure—the routine bone density scan—into a powerful early warning system for cardiovascular disease.[1][3]

The innovation centers on Dual-Energy X-ray Absorptiometry (DEXA) scans, which are widely used globally to assess bone health and diagnose osteoporosis. Researchers at Edith Cowan University (ECU) have successfully trained an artificial intelligence algorithm to analyze these exact same scans for a completely different threat: abdominal aortic calcification (AAC).[4][6]

AAC is a buildup of calcium in the walls of the abdominal aorta, the body's largest artery. Crucially, this calcification often appears years before similar blockages develop in the heart's own arteries or the brain's blood vessels. By spotting AAC early, doctors gain a critical window to intervene with lifestyle changes or medications long before a patient experiences a cardiac event.[2][5]

Historically, detecting this calcification required a highly trained specialist to manually review the scans—an expensive and time-consuming process that takes five to six minutes per image. Because of the cost and time involved, routine screening for AAC has remained largely out of reach for the general public.[2][3]

The new AI algorithm changes the math entirely. Developed by ECU Professor Joshua Lewis and his global team, the software can automatically detect and quantify AAC on lateral spine images in a matter of seconds. In tests, the AI processed thousands of images in under a minute, matching the diagnostic conclusions of human experts 80 percent of the time.[3][6]

Most importantly for patient safety, the algorithm proved highly sensitive to severe cases. Only 3 percent of individuals with high levels of calcification—those at the greatest risk for fatal cardiovascular events—were incorrectly categorized as low risk by the software. This high degree of accuracy makes the tool exceptionally well-suited for mass screening.[6]

The implications for public health are massive. In Australia alone, approximately 700,000 people undergo DEXA bone scans every year. Once the AI software is integrated into clinical practice, every single one of those appointments could simultaneously serve as a heart health check, requiring no additional radiation, no extra time in the clinic, and no separate specialist referral.[1][4]

The hidden nature of cardiovascular risk makes this dual-screening approach particularly vital. In a related study, ECU research fellow Dr. Cassandra Smith applied the algorithm to older patients undergoing routine bone testing and found that 58 percent had moderate to high levels of AAC. Alarmingly, one in four of those high-risk patients had absolutely no idea they were in danger.[2]

"Women are recognized as being under-screened and under-treated for cardiovascular disease," Dr. Smith noted, emphasizing that the AI tool could help close this gender gap in preventative care. Because women frequently receive DEXA scans for osteoporosis management, the algorithm provides a seamless pathway to catch cardiovascular risks that might otherwise go unnoticed.[2]

The benefits of detecting AAC extend beyond the heart. ECU senior research fellow Dr. Marc Sim discovered that patients with moderate to high calcification scores also face a significantly greater risk of fall-associated hospitalizations and bone fractures. The calcification serves as a broader biomarker for frailty, meaning the AI tool could help doctors design comprehensive care plans that protect both the cardiovascular and skeletal systems.[2]

For patients, the knowledge provided by the AI can be a powerful catalyst for change. Pippa Lukeis, a patient who discovered her own blood vessel disease through the scanning technology, described the diagnosis as a turning point. Because the calcification is entirely asymptomatic, she noted that without the scan, she would have continued blindly until the buildup caused a major health crisis.[1][4]

Armed with the early warning, Lukeis was able to completely reassess her lifestyle. She adopted a Mediterranean diet, incorporated regular cardio and resistance training into her routine, and began scheduling regular heart health checks. Her story exemplifies the ultimate goal of the technology: empowering individuals to take control of their health before a crisis forces their hand.[1][4]

Transitioning a major medical discovery from the laboratory to everyday clinical practice is notoriously difficult, but the ECU team is moving rapidly. The project recently secured funding through the Heart Foundation's Catalyst Partnership program, which aims to accelerate the rollout of the technology to clinics nationwide.[3][5]

Professor Garry Jennings, Chief Medical Advisor at the Heart Foundation, praised the discovery as a monumental step forward in preventative medicine. By leveraging existing infrastructure—the DEXA machines already sitting in hospitals and clinics worldwide—the AI tool bypasses the massive capital costs usually associated with deploying a new medical screening program.[3][5]

As healthcare systems globally grapple with aging populations and rising chronic disease burdens, innovations that multiply the value of existing tests are becoming increasingly crucial. By teaching an algorithm to look at an old scan with new eyes, researchers have unlocked a cost-effective, life-saving tool that could soon become a standard part of preventative care around the world.[1][5][6]