Somerset NHS Foundation Trust has become the first in the UK to test the performance of an AI algorithm for detecting lung cancer from x-rays.

“There’s been a lot of buzz about AI at radiology meetings, but there’s little experience of using it in an NHS trust,” says Dr Paul Burn, Consultant Radiologist at the trust. “We have a fairly elderly patient population, which may make it harder for AI imaging solutions to be effective because of a higher incidence of abnormalities that show up on x-rays, such as scarring and calcifications,” he adds.

By prioritizing which x-rays need urgent attention, AI helped reduce the time from chest x-ray to CT scan from 7 to 2.8 days, supporting the trust to meet its 28-day cancer diagnosis target.

The red dot® algorithm, which was developed in collaboration with NHS consultant radiologists, provides two outputs – a subset of abnormal x-rays with a high probability of lung cancer, and another subset of x-rays (high confidence normal) with a very high likelihood of being normal.

Of the 3,794 chest exams, the red dot® service classified 562 (14.8%) as high confidence normal (HCN). In 13 cases, radiologists disagreed with the model’s classification as HCN, a negative predictive value (NPV) of 97.7%. None of these discrepancies were considered clinically significant.

“High Confidence Normal results are an obvious opportunity for where AI can be used in the future,” he says. “Particularly for trusts with a big backlog reporting problem.’’

Simon Rasalingham, CEO and Chairman of Behold.AI, said, “Early stage lung cancers are often missed by x-rays. We believe that our technology can pick up 22,000 more cases of lung cancer every year, giving these people a significantly better chance of beating the disease.”