Thousands of people every year have an echocardiogram – a type of heart scan – after visiting hospital suffering with chest pain. Clinicians currently assess these scans by eye, taking into account many features that could indicate whether someone has heart disease and if they are likely to go on to have a heart attack. But even the most well trained cardiologist can misdiagnose patients. Currently, 1 in 5 scans are misdiagnosed each year – the equivalent to 12,000 patients. This means that people are either not being treated to prevent a heart attack, or they are undergoing unnecessary operations to stave off a heart attack they won’t have.
The new system uses machine learning – a form of artificial intelligence – to tap into the rich information provided in an echocardiogram. Using the new system, AI can detect 80,000 subtle changes inviable to the naked eye, improving the accuracy of diagnosis to 90%. The machine learning system was trained using scans from previous patients, alongside data about whether they went on to have a heart attack. The team hope that the improved diagnostic accuracy will not only improve patient care and outcomes, but save the NHS £300million a year in avoidable operations and treatment.
Ross Upton discusses the new system with BBC World News.
So far the system has been trialled in six cardiology units in the UK. Further implementation of the technology is now being led by Ultromics – a spin-out company co-founded by Ross Upton and Paul Leeson (Cardiovascular Clinical Research Facility). The software will be made available for free throughout the NHS later this year.