A new University College London Hospitals NHS Foundation Trust (UCLH) study is hoping to reduce the time taken to diagnose lung cancer by using artificial intelligence (AI) software to detect abnormalities on chest X-rays.
GPs in England request around two million chest X-rays every year and the results of most come back as normal.
But, for a small group of patients, there may be early indications of lung cancer.
And, once these signs are picked up, the next stage is referral for a CT scan of the chest to help confirm, or rule out, a diagnosis of lung cancer.
Real-world evidence from collaborative trials such as LungIMPACT is vital to help power confidence in digital health innovations and improve speed of cancer care for patients now, and into the future
The LungIMPACT trial will evaluate AI technology called qXR which is designed to spot potential abnormalities on chest X-rays.
The study is being co-led by Dr Nick Woznitza, a consultant radiographer at University College London Hospitals NHS Foundation Trust (UCLH) and clinical academic in the School of Allied and Public Health at Canterbury Christ Church University; and Professor David Baldwin, an honorary professor of medicine at Nottingham University Hospitals NHS Trust (NUH) and the University of Nottingham.
Reducing waiting times
The AI system, developed by Qure.ai, is designed to flag which patients would benefit from an urgent review of their scan by a reporting radiographer, who can arrange for a same-day CT scan if their X-ray indicates possible lung cancer.
In the study, the AI system will produce a secondary image for each chest X-ray, with an overlay to highlight certain abnormalities.
And, if the AI detects a problem, this will highlight the X-ray on the reporting worklist so the reporting radiographer can prioritise this for urgent reporting.
Studies evaluating the clinical impact of AI are urgently needed to ensure the safe and effective implementation that is needed to help the NHS and our patients
The research team will evaluate whether use of this AI ‘triage’ can successfully bring down time diagnosis times.
Dr Woznitza said: “The quicker we pick up on any potential anomalies on a chest X-ray, the better.
“We review X-ray results as quickly as possible, but if this technology can help us prioritise who would most benefit from a rapid review of their X-rays, this would help improve outcomes for our patients.”
Meeting the challenge
Professor Baldwin added: “Studies evaluating the clinical impact of AI are urgently needed to ensure the safe and effective implementation that is needed to help the NHS and our patients.
“Doing these studies is a significant challenge, but a worthwhile one.”
And Darren Stephens, senior vice president and commercial head for the UK and Europe at Qure.ai, said: “Trust in healthcare AI as a tool for supporting clinical case prioritisation is growing.
“Real-world evidence from collaborative trials such as LungIMPACT is vital to help power confidence in digital health innovations and improve speed of cancer care for patients now, and into the future.”
For the study, the qXR software has been integrated into imaging and health record systems by UCLH’s digital healthcare team.
It will analyse chest X-rays of adult patients referred to UCLH by their GPs.
The work is being commissioned and funded by the NHS Cancer Programme, with the support of SBRI Healthcare and the NHS Accelerated Access Collaborative and is supported by the National Institute for Health and Care Research Biomedical Research Centre at UCLH.
- Companies:
- University of Nottingham
- Nottingham University Hospitals NHS Trust
- UCLH NHS Foundation Trust
