AI Tool Detects Heart Condition Before Symptoms Appear, Reducing Stroke Risk
Published on 1/8/2025 | 3 min read
Published on 1/8/2025 | 3 min read
A groundbreaking artificial intelligence (AI) tool is now being used to identify individuals at risk of developing atrial fibrillation (AF) before symptoms appear. This innovative tool analyzes GP records to detect "red flags" indicating an increased risk of AF, a heart condition closely linked to a higher likelihood of strokes.
AF is characterized by an irregular and often rapid heartbeat. It affects approximately 1.6 million people in the UK, with thousands more potentially undiagnosed. Early detection is crucial as it allows for effective management, significantly reducing the risk of stroke.
John Pengelly, a 74-year-old former Army captain from Bradford, is one of the many individuals whose AF was detected thanks to this AI trial. Pengelly shared his gratitude for the early diagnosis, explaining that he now takes daily medication to manage his heightened stroke risk.
Despite having no symptoms of AF and feeling well, the AI tool identified his risk. Pengelly, who served in the Army Catering Corps for 29 years, credits the trial for potentially extending his life.
The AI tool was developed by a team of scientists and clinicians at the University of Leeds and Leeds Teaching Hospitals NHS Trust. It is currently being tested in the Find-AF trial, supported by funding from the British Heart Foundation (BHF) and Leeds Hospitals Charity.
The trial, taking place in several GP surgeries across West Yorkshire, uses the AI tool to analyze patient records and detect potential risk factors for AF. The algorithm evaluates data such as:
By identifying individuals at higher risk of AF, the AI tool helps ensure they receive timely medical attention to prevent strokes.
Atrial fibrillation is a leading cause of strokes in the UK, contributing to an estimated 20,000 strokes annually. Many individuals remain unaware of their AF until they experience a stroke, which often leads to severe consequences.
Early detection through AI has the potential to prevent these devastating events. According to Professor Chris Gale, a cardiovascular expert at the University of Leeds, early diagnosis can help avoid life-altering outcomes for patients and their families.
Dr. Ramesh Nadarajah, a consultant at Leeds Teaching Hospitals NHS Trust, highlighted that the West Yorkshire study could serve as the foundation for a nationwide initiative. The ultimate goal is to reduce the number of strokes caused by undiagnosed AF by increasing early diagnoses and ensuring at-risk individuals receive necessary treatment.
The AI tool represents a significant advancement in the early detection of atrial fibrillation. With its ability to save lives by preventing strokes, the technology has the potential to transform healthcare outcomes in the UK and beyond. As trials expand and the technology matures, this approach could lead to earlier diagnoses, better treatments, and a reduced impact of strokes on individuals and families.