ArticleArchives of osteoporosis2021
Natural language processing of radiology reports for the identification of patients with fracture.
Article in Archives of osteoporosis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis.BMJ open · 2023Pooled it
- Let's ask the patient: disease prediction based on patients' symptom descriptions in free text.Rheumatology advances in practice · 2025Article
- Question-based computational language approach outperforms rating scales in quantifying emotional states.Communications psychology · 2024Article
- Review
- Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images.Diagnostics (Basel, Switzerland) · 2023Article
- Approach to the Patient With Bone Fracture: Making the First Fracture the Last.The Journal of clinical endocrinology and metabolism · 2023Article
- Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports.Scientific reports · 2023Article
- Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions.Wireless personal communications · 2023Review
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- Early-stage COVID-19 pandemic observations on pulmonary embolism using nationwide multi-institutional data harvesting.NPJ digital medicine · 2022Article
- Electronic search programs are effective in identifying patients with minimal trauma fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2022Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Text-search software can be used to identify people at risk of re-fracture. The software studied identified a threefold higher number of people with fractures compared with conventional case finding. Automated software could assist fracture liaison services to identify more people at risk than traditional case finding. PURPOSE: 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).
methodsXRAIT 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.
resultsXRAIT 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%.
conclusionXRAIT 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.
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