AI Tool Detects Heart Condition Before Symptoms Appear

The FIND-AF algorithm identifies atrial fibrillation risk using medical records, aiming to prevent thousands of strokes annually.

The FIND-AF tool, developed by researchers at the University of Leeds, uses machine learning to analyze anonymized medical records for signs of atrial fibrillation (AF).
AF, a heart condition that increases stroke risk, often has no symptoms, leaving many individuals undiagnosed and vulnerable.
The algorithm evaluates factors like age, sex, ethnicity, and pre-existing conditions to identify patients at high risk of developing AF.
High-risk individuals are provided with handheld ECG devices to monitor heart rhythms, with abnormal results alerting their GP for further action.
Experts hope the West Yorkshire trial will lead to nationwide adoption, potentially reducing the 20,000 strokes linked to AF in the UK each year.