Artificial intelligence has been used for the first time to instantly and accurately measure blood flow, in a new study involving researchers from Queen Mary University of London.
The results were found to be able to predict chances of death, heart attack and stroke, and can be used by doctors to help recommend treatments, which could improve a patient’s blood flow.
In the largest study of its kind, published in the journal Circulation, researchers took routine CMR scans from more than 1,000 patients attending St Bartholomew's Hospital and the Royal Free Hospital and used a new automated artificial intelligence technique to analyse the images.
By doing this, the teams were able to precisely and instantaneously quantify the blood flow to the heart muscle and deliver the measurements to the medical teams treating the patients.
The research team, led by Barts Health NHS Trust and University College London, and including researchers from Royal Free Hospital, Queen Mary University of London and the University of Leeds, compared AI-generated blood flow results with the health outcomes of each patient.
They found that the patients with reduced blood flow were more likely to have adverse health outcomes including death, heart attack, stroke and heart failure.
The AI technique was therefore shown for the first time to be able to predict which patients might die or suffer major adverse events, better than a doctor could on their own with traditional approaches.
The project used data from the Barts BioResource, a research, audit and educational health resource, which supports research into cardiovascular diseases.
Professor Steffen Petersen, Professor of Cardiovascular Medicine at the William Harvey Research Institute at Queen Mary University of London and an author of the study, said: “This pioneering work shows that AI can successfully be used in heart image analysis to predict outcomes. It also demonstrates how existing data like the Barts BioResource, can be used effectively to support novel research studies, and the importance of initiatives like the Barts Heart Centre to bring expert clinicians and academics across different institutions together to make a real difference to patients with heart disease.”
Heart disease is the leading global cause of death and illness. Reduced blood flow, which is often treatable, is a common symptom of many heart conditions. International guidelines therefore recommend a number of assessments to measure a patient’s blood flow, but many are invasive and carry a risk.
Non-invasive blood flow assessments are available, including Cardiovascular Magnetic Resonance (CMR) imaging, but up until now, the scan images have been incredibly difficult to analyse in a manner precise enough to deliver a prognosis or recommend treatment.
Professor James Moon from Barts Health NHS Trust and University College London said: “Artificial intelligence is moving out of the computer labs and into the real world of healthcare, carrying out some tasks better than doctors could do alone. We have tried to measure blood flow manually before, but it is tedious and time-consuming, taking doctors away from where they are needed most, with their patients.”
Dr Kristopher Knott from Barts Health NHS Trust and University College London added: “The predictive power and reliability of the AI was impressive and easy to implement within a patient’s routine care. The calculations were happening as the patients were being scanned, and the results were immediately delivered to doctors. As poor blood flow is treatable, these better predictions ultimately lead to better patient care, as well as giving us new insights into how the heart works.”
The study was funded by the British Heart Foundation, National Institute for Health Research, European Regional Development Fund and Barts Charity.
More information:
For media information, contact: