Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm

Abstract

Aim: To evaluate the suitability of a deep-learning (DL) algorithm for identifying normality as a rule-out test for fully automated diagnosis in frontal adult chest radiographs (CXR) in an active clinical pathway.

Materials and methods: This multicentre study included 3,887 CXRs from four distinct NHS institutions. A convolutional neural network (CNN) was developed and trained prior to this study and was used to classify a subset of examinations with the lowest abnormality scores as high confidence normal (HCN). For each radiograph, the ground truth (GT) was established using two independent reviewers and an arbitrator in case of discrepancy.

Results: The DL algorithm was able to classify 15% of all examinations as HCN, with a corresponding precision of 97.7%. There were 0.33% of examinations classified incorrectly as HCN, with 84.6% of these examinations identified as borderline cases by the radiologist GT process.

Conclusion: A DL algorithm can achieve a high level of precision as a fully automated diagnostic tool for reporting a subset of CXRs as normal. The removal of 15% of all CXRs has the potential to significantly reduce workload and focus radiology resources on more complex examinations. To optimise performance, site-specific deployment of algorithms should occur with robust feedback mechanisms for incorrect classifications.