Scientists develop AI tool to spot heart failure risk five years before it strikes

2026-04-08 · Source: AI (artificial intelligence) | The Guardian · Field: Health & Wellbeing — Medical Devices & Health Technology, Clinical Care & Medical Practice, Health & Medical Research · Depth: Fundamental Awareness, short

Summary

Oxford scientists have developed an AI tool that predicts heart failure risk five years in advance by analyzing routine cardiac CT scans. The tool identifies invisible signs of inflammation and unhealthy fat around the heart, which are not detectable by the human eye. Validated on 72,000 patients across nine NHS trusts, the AI achieved 86% accuracy in predicting heart failure risk over a five-year period. Patients in the highest risk group were 20 times more likely to develop the condition, with a one in four chance within five years. The research, led by Professor Charalambos Antoniades, aims for regulatory approval to integrate this tool into hospital radiology departments, potentially allowing earlier intervention and improved patient management.

Key takeaway

For cardiologists and radiologists evaluating cardiac CT scans, this AI tool offers a significant advancement in identifying heart failure risk up to five years before onset. You should consider advocating for the integration of such AI-powered diagnostics into routine workflows to enable earlier intervention and more targeted patient management, potentially preventing severe heart damage and improving long-term patient health outcomes.

Key insights

An AI tool predicts heart failure risk five years early by analyzing cardiac CT scans for invisible fat inflammation.

Principles

• AI can detect sub-visual disease markers.
• Early risk stratification improves patient outcomes.

Method

The AI tool analyzes routine cardiac CT scans to identify signs of inflamed, unhealthy fat around the heart, generating an absolute risk score for heart failure development within five years.

In practice

• Integrate AI into routine CT scan analysis.
• Prioritize monitoring for high-risk patients.

Topics

• AI Diagnostics
• Heart Failure Prediction
• Cardiac CT Scans
• Pericardial Fat Analysis
• Cardiovascular Risk Assessment

Best for: Computer Vision Engineer, AI Scientist, Research Scientist

Related on AIssential

• Can AI help predict which heart-failure patients will worsen within a year? — PULSE-HF is a deep-learning model forecasting heart failure worsening via ECGs, achieving high accuracy in LVEF decline prediction.
• Sybil: An AI model that can predict lung cancer risk 6 years in advance — Sybil AI predicts future lung cancer risk from CT scans by analyzing subtle, comprehensive image data.
• Could AI Imaging Spot Pregnancy Health Risks Earlier? — AI can significantly enhance early detection of high-risk pregnancy conditions like PAS and CHD, improving care pathways.
• Doctors May Soon Be Able to Diagnose Illnesses Before You Ever Feel Sick — AI can predict disease onset by monitoring dynamic molecular network instability before symptoms appear.
• Improving breast cancer screening workflows with machine learning — Google Research, in partnership with NHS organizations, conducted the Artificial Intelligence in Mammography Screening (AIMS) study, publishing two companion studies in Nature Cancer to evaluate an AI…
• End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT — An end-to-end pipeline automates heart failure diagnosis from German clinical notes using zero-shot learning and SNOMED-CT.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI (artificial intelligence) | The Guardian.