Natural language processing of radiology reports for the identification of patients with fracture.

Abstract

Fracture liaison services address the post-fracture treatment gap in osteoporosis (OP). Natural language processing (NLP) is able to identify previously unrecognized patients by screening large volumes of radiology reports. The aim of this study was to compare an NLP software tool, XRAIT (X-Ray Artificial Intelligence Tool), with a traditional fracture liaison service at its development site (Prince of Wales Hospital [POWH], Sydney) and externally validate it in an adjudicated cohort from the Dubbo Osteoporosis Epidemiology Study (DOES). XRAIT searches radiology reports for fracture-related terms. At the development site (POWH), XRAIT and a blinded fracture liaison clinician (FLC) reviewed 5,089 reports and 224 presentations, respectively, of people 50 years or over during a simultaneous 3-month period. In the external cohort of DOES, XRAIT was used without modification to analyse digitally readable radiology reports (n = 327) to calculate its sensitivity and specificity. XRAIT flagged 433 fractures after searching 5,089 reports (421 true fractures, positive predictive value of 97%). It identified more than a threefold higher number of fractures (421 fractures/339 individuals) compared with manual case finding (98 individuals). Unadjusted for the local reporting style in an external cohort (DOES), XRAIT had a sensitivity of 70% and specificity of 92%. XRAIT identifies significantly more clinically significant fractures than manual case finding. High specificity in an untrained cohort suggests that it could be used at other sites. Automated methods of fracture identification may assist fracture liaison services so that limited resources can be spent on treatment rather than case finding.