Leeds-based researchers have developed a groundbreaking artificial intelligence (AI) system that could revolutionise the early detection of heart failure, allowing for timely treatment. The algorithm, named Find-HF, has been meticulously trained by experts from the University of Leeds using extensive patient records.
According to the British Heart Foundation (BHF), heart failure affects over one million individuals in the UK. The new AI technology aims to address this significant health issue by identifying early symptoms and high-risk patients.
Professor Chris Gale, from Leeds Teaching Hospitals NHS Trust and the University of Leeds, emphasised the importance of this innovation, stating that it provides a “crucial window of opportunity” for early intervention. This could potentially allow diagnoses to be made up to two years earlier than current methods permit.
The research, generously funded by the BHF, utilised the health records of 565,284 UK adults to train the Find-HF algorithm. Its effectiveness was further validated using an additional 106,026 patient records from Taiwan National University Hospital. The AI demonstrated a high accuracy rate in predicting which patients were most at risk of developing heart failure or being hospitalised due to the condition within a five-year period.
Professor Gale, a consultant cardiologist, described the data set as an “extremely powerful and unique national resource” that should be harnessed to benefit patients. He highlighted the potential of Find-HF to transform the way general practitioners (GPs) identify and diagnose heart failure, advocating for its use as an early warning system.
Dr. Ramesh Nadarajah, a health data research UK fellow at the University of Leeds, pointed out the current challenges in diagnosing heart failure, particularly among women and older adults who often receive their diagnosis at an advanced stage. Dr Nadarajah explained that by leveraging machine learning tools and routinely collected health data, Find-HF aims to identify heart failure at an earlier stage, thereby enhancing treatment efficacy, preventing hospital admissions, reducing mortality rates, and improving overall quality of life.
