ArticleSkeletal radiology2022
Natural language processing of radiology reports to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures.
Article in Skeletal radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports.Computational and structural biotechnology journal · 2024Article
- Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing.Insights into imaging · 2023Article
- Effects of COVID-19 confinement on the incidence and mortality of major osteoporotic fractures: an observational study in Catalonia, Spain.Archives of osteoporosis · 2022Observational
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDuring the COVID-19 pandemic, the number of patients presenting in hospitals because of emergency conditions decreased. Radiology is thus confronted with the effects of the pandemic. The aim of this study was to use natural language processing (NLP) to automatically analyze the number and distribution of fractures during the pandemic and in the 5 years before the pandemic. MATERIALS AND
methodsWe used a pre-trained commercially available NLP engine to automatically categorize 5397 radiological reports of radiographs (hand/wrist, elbow, shoulder, ankle, knee, pelvis/hip) within a 6-week period from March to April in 2015-2020 into "fracture affirmed" or "fracture not affirmed." The NLP engine achieved an F
resultsIn 2020, we found a significant decrease of fractures in general (p < 0.001); the average number of fractures in 2015-2019 was 295, whereas it was 233 in 2020. In children and adolescents (p < 0.001), and in adults up to 65 years (p = 0.006), significantly fewer fractures were reported in 2020. The number of fractures in the elderly did not change (p = 0.15). The number of hand/wrist fractures (p < 0.001) and fractures of the elbow (p < 0.001) was significantly lower in 2020 compared with the average in the years 2015-2019.
conclusionNLP can be used to identify relevant changes in the number of pathologies as shown here for the use case fracture detection. This may trigger root cause analysis and enable automated real-time monitoring in radiology.
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