Automated decision making in Barrett’s oesophagus: development and deployment of a natural language processing tool

Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett’s oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to ca...

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Bibliographic Details
Published in:	NPJ digital medicine Vol. 7; no. 1; pp. 312 - 9
Main Authors:	Zecevic, Agathe, Jackson, Laurence, Zhang, Xinyue, Pavlidis, Polychronis, Dunn, Jason, Trudgill, Nigel, Ahmed, Shahd, Visaggi, Pierfrancesco, YoonusNizar, Zanil, Roberts, Angus, Zeki, Sebastian S.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 07-11-2024 Nature Publishing Group Nature Portfolio
Subjects:	631/114/1305 692/699/1503/1476/1322 Biomedicine Biotechnology Medicine Medicine & Public Health
Online Access:	Get full text
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Summary:	Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett’s oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy’s and St Thomas’ Hospital (GSTT). The accuracies for EndoBERT test sets from GSTT, King’s College Hospital (KCH), and Sandwell and West Birmingham Hospitals (SWB) were 0.95, 0.86, and 0.99, respectively. Average accuracies for PathBERT were 0.93, 0.91, and 0.92, respectively. A retrospective analysis of 1640 GSTT reports revealed a 27% discrepancy between endoscopists’ decisions and model recommendations. This study underscores the development and deployment of NLP-based software in BO surveillance, demonstrating high performance at multiple sites. The analysis emphasizes the potential efficiency of automation in enhancing precision and guideline adherence in clinical decision-making.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2398-6352 2398-6352
DOI:	10.1038/s41746-024-01302-6