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

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.

Citation

Moezzi, S. A. R., Ghaedi, A., Rahmanian, M., Mousavi, S. Z., & Sami, A. (2023). Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique. Journal of Digital Imaging, 36(1), 80-90. https://doi.org/10.1007/s10278-022-00692-x

• 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