AI Predicts Heart Failure Risk Five Years Out

Researchers at the University of Oxford have created an artificial intelligence tool designed to estimate a patient’s risk of developing heart failure as far as five years ahead, using routine cardiac CT scans. The work was described in the Journal of the American College of Cardiology on Wednesday. According to the researchers, the system detects subtle signs linked to unhealthy fat around the heart that are not visible to the human eye on standard review.

The team said the model was trained and validated on data from 72,000 patients drawn from nine NHS trusts in England. Those patients were followed for a decade after their CT scans, providing long-term outcomes that could be compared with what the AI flagged in the imaging. The researchers reported that the tool reached an 86% accuracy rate for predicting whether heart failure would develop within a five-year window.

Beyond overall performance, the study also reported a sharp difference between risk groups. Patients classified as high-risk by the AI were found to be 20 times more likely to develop heart failure than those placed in the lowest-risk category. The researchers said the high-risk group faced roughly a one in four chance of developing the condition within five years.

The Oxford group said the tool produces a patient-specific risk score intended to support clinical decision-making. In practice, that score could help clinicians decide which patients may need closer monitoring and earlier intervention strategies, based on the risk level identified from the CT scan. The researchers framed the approach as a way to extract additional clinical value from imaging that is already being performed.

The team is now seeking regulatory approval to integrate the technology into healthcare systems, including the NHS. They also said they aim to expand the approach so it can be applied to any chest CT scan, not only dedicated cardiac CT imaging. If approved and adopted, the tool could influence how CT imaging is used in preventive care pathways by adding automated risk stratification to existing workflows.

Key uncertainties remain tied to the next steps described by the researchers. Regulatory review, implementation timelines, and how the tool would be deployed across different clinical settings were not specified in the report. The researchers’ stated goal is to move from publication to real-world use, but the pace and scope of integration will depend on approval processes and health-system decisions.