Somerset NHS Foundation Trust has used a new AI algorithm to detect signs of lung cancer in x-rays.
It has run a trial using the red dot algorithm from Behold.AI that has provided evidence this could halve the time from initial x-ray screening to a CT scan.
The company said that over three months the trust found that from 3,794 images reviewed by the algorithm, the average time from one scan to the other was reduced from seven to 2.8 days, and the results went into the hospital systems in just 16 seconds.
The algorithm classified 562 cases as high confidence normal, from which radiologists disagreed with just 13. None of the discrepancies were considered to be clinically significant.
This could help the trust meet its 28-day target for cancer diagnosis.
Buzz but little experience
Dr Paul Burn, consultant radiologist at the trust, said: “There’s been a lot of buzz about AI at radiology meetings, but there’s little experience of using it in an NHS trust. We embarked on a bottom-up initiative to test the algorithm, with the aim of helping us improve our referral times.
“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 added: “High confidence normal results are an obvious opportunity for where AI can be used in the future, particularly for trusts with a big backlog reporting problem.’’
The red dot algorithm was developed in collaboration with NHS consultant radiologists and provides two outputs: a subset of abnormal x-rays with a high probability of lung cancer, and another subset of high confidence normal x-rays with a very high likelihood of being normal.
Beating the disease
Simon Rasalingham, CEO and chair 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.”
The trial was funded by the Somerset, Wiltshire Avon and Gloucestershire Cancer Alliance (SWAG).
