Identifying abdominal aortic aneurysm size and presence using Natural Language Processing of radiology reports: a systematic review and meta-analysis

Abstract

Background and aim

Prior investigations of the natural history of abdominal aortic aneurysms (AAAs) have been constrained by small sample sizes or uneven assessments of aggregated data. Natural language processing (NLP) can significantly enhance the investigation and treatment of patients with AAAs by swiftly and effectively collecting imaging data from health records. This meta-analysis aimed to evaluate the efficacy of NLP techniques in reliably identifying the existence or absence of AAAs and measuring the maximal abdominal aortic diameter in extensive datasets of radiology study reports.

Method

The PubMed, Scopus, Web of Science, Embase, and Science Direct databases were searched until March 2024 to obtain pertinent papers. The RAYYAN intelligent tool for systematic reviews was utilized to screen the studies. The meta-analysis was conducted using STATA v18 software. Egger’s test was employed to evaluate publication bias. The Newcastle Ottawa Scale was employed to assess the quality of the listed studies. A plot digitizer was employed to extract digital data.

Result

A total of 39,094 individuals with AAA were included in this analysis. Twenty-seven thousand three hundred twenty-six patients were male, and 11,383 were female. The mean age of the total participants was 73.1 ± 1.25 years. Analysis results for pooled estimation of performance variables such as: The sensitivity, specificity, precision, and accuracy of the implemented NLP model were analyzed as follows: 0.89(0.88–0.91), 0.88 (0.87–0.89), 0.92 (0.89–0.95), and 0.91 (0.89–0.93) respectively. The aneurysm diameter size difference reported in follow-up before and after NLP implementation in the included studies showed a 0.05 cm reduction in size, which was statistically significant.

Conclusion

NLP holds great potential for automating the detection of AAA size and presence in radiology reports, enhancing efficiency and scalability over manual review. However, challenges persist. Variability in report formats, terminology, and unstructured data can compromise accuracy. Additionally, NLP models rely on high-quality, annotated training datasets, which may be incomplete or unrepresentative. While NLP aids in identifying AAA-related data, human oversight is essential to ensure decisions are informed by the patient’s broader clinical context. Ongoing algorithm refinement and seamless integration into clinical workflows are key to improving NLP’s utility and reliability in this field.