Real-World Performance of Autonomously Reporting Normal Chest
  Radiographs in NHS Trusts Using a Deep-Learning Algorithm on the GP Pathway
Jordan Smith Tom Naunton Morgan Paul Williams Qaiser Malik Simon Rasalingham
AIM To analyse the performance of a deep-learning (DL) algorithm currently
deployed as diagnostic decision support software in two NHS Trusts used to
identify normal chest x-rays in active clinical pathways.
  MATERIALS AND METHODS A DL algorithm has been deployed in Somerset NHS
Foundation Trust (SFT) since December 2022, and at Calderdale & Huddersfield
NHS Foundation Trust (CHFT) since March 2023. The algorithm was developed and
trained prior to deployment, and is used to assign abnormality scores to each
GP-requested chest x-ray (CXR). The algorithm classifies a subset of
examinations with the lowest abnormality scores as High Confidence Normal
(HCN), and displays this result to the Trust. This two-site study includes
4,654 CXR continuous examinations processed by the algorithm over a six-week
period.
  RESULTS When classifying 20.0% of assessed examinations (930) as HCN, the
model classified exams with a negative predictive value (NPV) of 0.96. There
were 0.77% of examinations (36) classified incorrectly as HCN, with none of the
abnormalities considered clinically significant by auditing radiologists. The
DL software maintained fast levels of service to clinicians, with results
returned to Trusts in a mean time of 7.1 seconds.
  CONCLUSION The DL algorithm performs with a low rate of error and is highly
effective as an automated diagnostic decision support tool, used to
autonomously report a subset of CXRs as normal with high confidence. Removing
20% of all CXRs reduces workload for reporters and allows radiology departments
to focus resources elsewhere.