Algorithm finds heart patients without symptoms, raising new health questions in West Yorkshire
- A new AI tool developed in the UK can identify individuals at risk of atrial fibrillation before they show symptoms, reviewing GP records for relevant indicators.
- About 1.6 million people are diagnosed with atrial fibrillation in the UK, but many remain undiagnosed and untreated, risking strokes.
- The initiative aims to increase early diagnoses and treatment of atrial fibrillation, potentially leading to a significant decrease in stroke incidents.
In the UK, health authorities have launched a groundbreaking AI tool designed to detect atrial fibrillation (AF) before symptoms appear. The tool is currently being tested in a six-month trial at select GP surgeries in West Yorkshire, developed by the University of Leeds and Leeds Teaching Hospitals NHS Trust. It analyzes patient data for risk factors such as age, sex, and existing medical conditions. This innovative approach aims to identify individuals at risk for AF, which has been linked to a significantly higher chance of stroke. Around 1.6 million individuals in the UK are diagnosed with AF, yet many remain unaware they have the condition. Current projections suggest thousands of undiagnosed patients could be identified through this tool, enabling early treatment that significantly reduces stroke risks. Following successful trials, there is potential for a broader UK-wide implementation of the tool, indicating a major advancement in stroke prevention efforts. The British Heart Foundation indicates that effective treatments for AF can diminish stroke risks considerably, emphasizing the need for early identification. NHS England suggests that enhancing the number of patients receiving anticoagulation therapy has already led to a significant reduction in stroke incidents over recent years, demonstrating the importance of proactive measures in cardiovascular health.