ArticleJMIR medical informatics2022
Natural Language Processing for Assessing Quality Indicators in Free-Text Colonoscopy and Pathology Reports: Development and Usability Study.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Using Large Language Models for Text Classification in Colonoscopy Pathology Reports.Gastro hep advances · 2026Review
- Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records.BMJ open gastroenterology · 2025Article
- A Novel Ensemble Framework for Comprehensive Early-Stage Colorectal Cancer Diagnosis, Prognosis, and Treatment: Integration of Gastroenterology-Specific Transformer Language Models and Multiple Decision Trees.Journal of clinical medicine · 2025Article
- Multimodal identification of a rare head and neck cancer patient cohort in the clinical data warehouse of Greater Paris Teaching Hospital.ESMO real world data and digital oncology · 2025Article
- De Novo Natural Language Processing Algorithm Accurately Identifies Myxofibrosarcoma From Pathology Reports.Clinical orthopaedics and related research · 2025Article
- Knowledge, Attitudes, and Practices of Patients Undergoing Gastrointestinal Endoscopy Towards Painless Endoscopy: A Cross-Sectional Study.Patient preference and adherence · 2025Article
- Performance of natural language processing in identifying adenomas from colonoscopy reports: a systematic review and meta-analysis.iGIE : innovation, investigation and insights · 2023Article
- Deep learning approach to detection of colonoscopic information from unstructured reports.BMC medical informatics and decision making · 2023Article
- Leveraging Natural Language Processing to Extract Features of Colorectal Polyps From Pathology Reports for Epidemiologic Study.JCO clinical cancer informatics · 2023Article
- Key quality indicators in colonoscopy.Gastroenterology report · 2023Review
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- Natural Language Processing for Information Extraction of Gastric Diseases and Its Application in Large-Scale Clinical Research.Journal of clinical medicine · 2022Article
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10 authors.
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Abstract
backgroundManual data extraction of colonoscopy quality indicators is time and labor intensive. Natural language processing (NLP), a computer-based linguistics technique, can automate the extraction of important clinical information, such as adverse events, from unstructured free-text reports. NLP information extraction can facilitate the optimization of clinical work by helping to improve quality control and patient management.
objectiveWe developed an NLP pipeline to analyze free-text colonoscopy and pathology reports and evaluated its ability to automatically assess adenoma detection rate (ADR), sessile serrated lesion detection rate (SDR), and postcolonoscopy surveillance intervals.
methodsThe NLP tool for extracting colonoscopy quality indicators was developed using a data set of 2000 screening colonoscopy reports from a single health care system, with an associated 1425 pathology reports. The NLP system was then tested on a data set of 1000 colonoscopy reports and its performance was compared with that of 5 human annotators. Additionally, data from 54,562 colonoscopies performed between 2010 and 2019 were analyzed using the NLP pipeline.
resultsThe NLP pipeline achieved an overall accuracy of 0.99-1.00 for identifying polyp subtypes, 0.99-1.00 for identifying the anatomical location of polyps, and 0.98 for counting the number of neoplastic polyps. The NLP pipeline achieved performance similar to clinical experts for assessing ADR, SDR, and surveillance intervals. NLP analysis of a 10-year colonoscopy data set identified great individual variance in colonoscopy quality indicators among 25 endoscopists.
conclusionsThe NLP pipeline could accurately extract information from colonoscopy and pathology reports and demonstrated clinical efficacy for assessing ADR, SDR, and surveillance intervals in these reports. Implementation of the system enabled automated analysis and feedback on quality indicators, which could motivate endoscopists to improve the quality of their performance and improve clinical decision-making in colorectal cancer screening programs.
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