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Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique

Moezzi, Seyed Ali Reza; Ghaedi, Abdolrahman; Rahmanian, Mojdeh; Mousavi, Seyedeh Zahra; Sami, Ashkan

Authors

Seyed Ali Reza Moezzi

Abdolrahman Ghaedi

Mojdeh Rahmanian

Seyedeh Zahra Mousavi

Ashkan Sami



Abstract

Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.

Journal Article Type Article
Acceptance Date Jul 27, 2022
Online Publication Date Aug 24, 2022
Publication Date 2023-02
Deposit Date Dec 4, 2022
Journal Journal of Digital Imaging
Print ISSN 0897-1889
Electronic ISSN 1618-727X
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 36
Issue 1
Pages 80-90
DOI https://doi.org/10.1007/s10278-022-00692-x
Keywords Structured reporting, Named entity recognition, Relation extraction, Natural language processing, Deep learning, Transformers
Public URL http://researchrepository.napier.ac.uk/Output/2972097