By C. J. De Mel, Jadetimes News
AI Technology for Early Detection of Heart Failure Developed by Leeds Researchers
Researchers based in Leeds have developed an innovative artificial intelligence (AI) technology that could revolutionize the early identification of patients at risk of heart failure. This advancement aims to enable earlier treatment interventions.
The Find HF Algorithm
The algorithm, named Find HF, has been meticulously trained by researchers at Leeds University to recognize early symptoms of heart failure through the analysis of patient records. This development is poised to make significant strides in the early diagnosis and management of heart failure.
Heart Failure Statistics in the UK
According to the British Heart Foundation (BHF), over one million individuals in the UK are currently living with heart failure. This underscores the urgent need for effective early detection and treatment strategies.
Potential Impact on Patient Care
Professor Chris Gale, affiliated with both Leeds Teaching Hospitals NHS Trust and the University of Leeds, emphasized the transformative potential of this technology. He stated that it could provide a "crucial window of opportunity" for patients, allowing for earlier intervention and potentially improving outcomes.
Research Methodology
The development and validation of the Find HF algorithm involved the use of patient records from 565,284 UK adults. Additionally, the algorithm was tested on a separate database containing 106,026 records from Taiwan National University Hospital. The AI demonstrated a high level of accuracy in predicting patients at the greatest risk of developing heart failure and those likely to be hospitalized with the condition within a five year period.
Enhanced Quality of Life
Professor Gale, a consultant cardiologist, highlighted the significance of this research, describing it as a "powerful and unique national resource." He noted that the use of Find HF could potentially advance diagnoses by up to two years, offering a valuable tool for early intervention.
Practical Applications in Healthcare
The researchers propose that the Find HF platform could serve as an early warning system for general practitioners (GPs), enabling them to test and diagnose patients at an earlier stage. This early detection capability could be instrumental in improving patient outcomes.
Addressing Late Stage Diagnoses
Dr. Ramesh Nadarajah, a Health Data Research UK fellow at the University of Leeds, pointed out that many patients receive their heart failure diagnosis at a late stage, when disease modifying treatments may be less effective, particularly among women and older individuals. By leveraging machine learning tools with routinely collected data, the team aims to identify heart failure earlier, ensuring that patients receive appropriate treatment, thereby preventing hospital admissions, reducing mortality, and enhancing quality of life.
