ArticleJournal of clinical gastroenterology2019
Accurate Identification of Colonoscopy Quality and Polyp Findings Using Natural Language Processing.
Article in Journal of clinical gastroenterology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 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
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Surveillance Colonoscopy Findings in Older Adults With a History of Colorectal Adenomas.JAMA network open · 2024Article
- Natural Language Processing Can Automate Extraction of Barrett's Esophagus Endoscopy Quality Metrics.medRxiv : the preprint server for health sciences · 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
- Natural Language Processing for Assessing Quality Indicators in Free-Text Colonoscopy and Pathology Reports: Development and Usability Study.JMIR medical informatics · 2022Article
- Impact of the Affordable Care Act on Colorectal Cancer Incidence and Mortality.American journal of preventive medicine · 2022Article
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation Using Combined Contextual Embeddings.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- Automated detection of cecal intubation with variable bowel preparation using a deep convolutional neural network.Endoscopy international open · 2021Article
- Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports.Gastrointestinal endoscopy · 2021Article
- Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.Digestive diseases and sciences · 2021Review
- Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.American journal of human genetics · 2020Article
- A Transparent and Adaptable Method to Extract Colonoscopy and Pathology Data Using Natural Language Processing.Journal of medical systems · 2020Article
- Long-term Risk of Colorectal Cancer and Related Death After Adenoma Removal in a Large, Community-based Population.Gastroenterology · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesThe aim of this study was to test the ability of a commercially available natural language processing (NLP) tool to accurately extract examination quality-related and large polyp information from colonoscopy reports with varying report formats.
backgroundColonoscopy quality reporting often requires manual data abstraction. NLP is another option for extracting information; however, limited data exist on its ability to accurately extract examination quality and polyp findings from unstructured text in colonoscopy reports with different reporting formats. STUDY
designNLP strategies were developed using 500 colonoscopy reports from Kaiser Permanente Northern California and then tested using 300 separate colonoscopy reports that underwent manual chart review. Using findings from manual review as the reference standard, we evaluated the NLP tool's sensitivity, specificity, positive predictive value (PPV), and accuracy for identifying colonoscopy examination indication, cecal intubation, bowel preparation adequacy, and polyps ≥10 mm.
resultsThe NLP tool was highly accurate in identifying examination quality-related variables from colonoscopy reports. Compared with manual review, sensitivity for screening indication was 100% (95% confidence interval: 95.3%-100%), PPV was 90.6% (82.3%-95.8%), and accuracy was 98.2% (97.0%-99.4%). For cecal intubation, sensitivity was 99.6% (98.0%-100%), PPV was 100% (98.5%-100%), and accuracy was 99.8% (99.5%-100%). For bowel preparation adequacy, sensitivity was 100% (98.5%-100%), PPV was 100% (98.5%-100%), and accuracy was 100% (100%-100%). For polyp(s) ≥10 mm, sensitivity was 90.5% (69.6%-98.8%), PPV was 100% (82.4%-100%), and accuracy was 95.2% (88.8%-100%).
conclusionNLP yielded a high degree of accuracy for identifying examination quality-related and large polyp information from diverse types of colonoscopy reports.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.