Innovative Machine Learning Algorithm Assesses Cardiovascular Risk Instantly

Recent advancements in cardiovascular disease detection leverage artificial intelligence and innovative diagnostic tools to enable faster, more accessible, and non-invasive risk assessment. Researchers from Edith Cowan University and the University of Manitoba developed a machine learning algorithm that can analyze bone density scans for signs of abdominal aortic calcification, a key marker of cardiovascular risk, dramatically reducing the time required for assessment and enabling dual-use screening for osteoporosis and heart disease. Another AI-powered app, Circadiav, created by a 14-year-old, detects heart disease by analyzing heartbeat audio recordings from smartphones, offering a simple and effective solution especially valuable in areas with limited medical infrastructure. Additionally, the EU-funded InSiDe project has produced a compact device using silicon photonics and laser Doppler vibrometry for real-time detection of arterial stiffness, promising reliable and immediate results without invasive procedures. These innovations collectively enhance early detection and intervention for cardiovascular disease, addressing a major global health challenge. Experts highlight that such technologies may especially benefit underdiagnosed groups, such as older women, by integrating risk assessment into routine clinical workflows.