Natural language processing of radiology reports to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures

Objective: During 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 methods: We 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 F₁ score of 0.81 compared to human annotators. Results: In 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. Conclusion: NLP 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.