A systematic review of natural language processing applied to radiology reports

BMC Medical Informatics and Decision Making

Emma Davidson¹  Michael Poon¹  William Whiteley²  Hang Dong³  Víctor Suárez-Paniagua³  Honghan Wu⁴  Richard Tobin⁵  Andreas Grivas⁵  Claire Grover⁵  Daniel Duma⁶  Arlene Casey⁶  Beatrice Alex⁷ 

[1] Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland;Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland;Nuffield Department of Population Health, University of Oxford, Oxford, UK;Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland;Health Data Research UK, London, UK;Health Data Research UK, London, UK;Institute of Health Informatics, University College London, London, UK;Institute for Language, Cognition and Computation, School of informatics, University of Edinburgh, Edinburgh, Scotland;School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland;School of Literatures, Languages and Cultures (LLC), University of Edinburgh, Edinburgh, Scotland;Edinburgh Futures Institute, University of Edinburgh, Edinburgh, Scotland;

关键词: Natural language processing;    Radiology;    Systematic review;   

DOI  :  10.1186/s12911-021-01533-7

来源: Springer

【 摘 要 】

BackgroundNatural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.MethodsWe conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.ResultsWe present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.ConclusionsAutomated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

【 授权许可】

CC BY