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.

A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing.

Structured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So far, however, this unstructured content cannot be categorized. We developed a solution to analyze and code these free-text parts of the templates in our MRRT-compliant reporting platform, using natural language processing (NLP) with RadLex® terms in addition to the already categorized items. The established hybrid reporting concept is working successfully. The NLP tool provides RadLex® codes with modifiers (affirmed, speculated, negated). Radiologists can confirm or reject codes provided by NLP before finalizing the structured report. Furthermore, users can suggest RadLex® codes from free text that is not correctly coded with NLP or can suggest to change the modifier. Analyzing free-text fields took 1.23 s on average. Hybrid reporting enables coding of free-text information in our MRRT-compliant templates and thus increases the amount of categorized data that can be stored in the database. This enhances the possibilities for further analyses, such as correlating clinical information with radiological findings or storing high-quality structured information for machine-learning approaches.

[Natural language processing in radiology : Neither trivial nor impossible]. / Natural Language Processing in der Radiologie : Weder trivial noch unerreichbare Magie.

BACKGROUND: The need for application expertise in natural language processing (NLP) is increasing in radiology. This way, in a complementary fashion to structured reporting using templates, the necessary database for quality assurance and continuous process optimization can be generated. OBJECTIVE: Possibilities and challenges of the application of NLP from the radiology point of view are explained. MATERIALS AND METHODS: The requirements and expectations for NLP systems are identified and demonstrated using a case study. RESULTS: For an effective use of this technology, NLP tasks for the interpretation of text using RadLex, an intuitive usage and feedback option as well as transparent quality of the NLP results are important. DISCUSSION: Using suitable NLP systems, targeted information can be extracted from large amounts of free text with manageable manual effort and high quality.

[Basics and applications of Natural Language Processing (NLP) in radiology]. / Grundlagen und Einsatzmöglichkeiten von Natural Language Processing (NLP) in der Radiologie.

BACKGROUND: Due to the increasing demands in radiology, applications that enable quality assurance and continuous process optimization are required. OBJECTIVE: The principles of Natural Language Processing (NLP) as a computer-based method for structuring of free text reports are explained and application scenarios are sketched. MATERIALS UND METHODS: The structuring of free texts succeeds by several theories, linguistic techniques (word meanings, word context, negations), statistical methods with rules and currently with deep learning approaches. Medical encyclopedias, such as RadLex®, are suitable for coding findings. NLP was used in our own radiology clinic to check the quality of 3756 CT reports. RESULTS: In our case study, NLP proved to be a helpful, automated tool for internal quality testing. DISCUSSION: NLP offers numerous application scenarios for decision support and for quality management in radiology.