AI-Powered Early Detection Could Revolutionize Heart Disease Diagnosis

Researchers at Graz University of Technology have created a new way to detect cardiovascular diseases early. The method uses machine learning to analyse electrical and optical signals from patients. This could help spot conditions before they become severe. The team developed a model that checks for signs of arterial stiffening, a warning sign for aortic dissection. By processing real clinical data and simulations, the system achieves over 90% accuracy. Once high-risk changes are found, a detailed digital twin of the patient is built.

This digital twin runs multiphysics simulations to predict how the disease might progress. The machine learning model also estimates the likelihood of specific conditions being present. The technology is now being prepared for wider use by arterioscope, a spin-off company bringing it to market.

The new approach could change how cardiovascular diseases are diagnosed. By catching problems early, doctors may intervene sooner and improve patient outcomes. The spin-off company aims to make the method widely available in the near future.