ArticleGastrointestinal endoscopy2015
Natural language processing as an alternative to manual reporting of colonoscopy quality metrics.
Article in Gastrointestinal endoscopy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Prevalence and Clinical Features of Sessile Serrated Polyps: A Systematic Review.Gastroenterology · 2020Pooled it
- Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records.BMJ open gastroenterology · 2025Article
- Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices.Digestion · 2024Article
- Development and Validation of an Automated, Real-time Adenoma Detection Rate and Colonoscopy Quality Metrics Calculator.Techniques and innovations in gastrointestinal endoscopy · 2024Article
- Performance of natural language processing in identifying adenomas from colonoscopy reports: a systematic review and meta-analysis.iGIE : innovation, investigation and insights · 2023Article
- 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
- Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.Diagnostics (Basel, Switzerland) · 2023Review
- Leveraging Natural Language Processing to Extract Features of Colorectal Polyps From Pathology Reports for Epidemiologic Study.JCO clinical cancer informatics · 2023Article
- Clinically Significant Metachronous Colorectal Pathology Detected Among Young-Onset Colorectal Cancer Survivors: Implications for Post-Resection Surveillance Guidelines.Gastroenterology · 2022Article
- Leveraging electronic medical record functionality to capture adenoma detection rate.Scientific reports · 2022Article
- Quality indicators in colonoscopy: the chasm between ideal and reality.Clinical endoscopy · 2022Review
- Natural Language Processing for Assessing Quality Indicators in Free-Text Colonoscopy and Pathology Reports: Development and Usability Study.JMIR medical informatics · 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
- Simplifying Measurement of Adenoma Detection Rates for Colonoscopy.Digestive diseases and sciences · 2021Article
- Consolidated EHR Workflow for Endoscopy Quality Reporting.Studies in health technology and informatics · 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
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Authors and funding
22 authors at 1 institution in 1 country.
Funding
Abstract
BACKGROUND AND
aimsThe adenoma detection rate (ADR) is a quality metric tied to interval colon cancer occurrence. However, manual extraction of data to calculate and track the ADR in clinical practice is labor-intensive. To overcome this difficulty, we developed a natural language processing (NLP) method to identify adenomas and sessile serrated adenomas (SSAs) in patients undergoing their first screening colonoscopy. We compared the NLP-generated results with that of manual data extraction to test the accuracy of NLP and report on colonoscopy quality metrics using NLP.
methodsIdentification of screening colonoscopies using NLP was compared with that using the manual method for 12,748 patients who underwent colonoscopies from July 2010 to February 2013. Also, identification of adenomas and SSAs using NLP was compared with that using the manual method with 2259 matched patient records. Colonoscopy ADRs using these methods were generated for each physician.
resultsNLP correctly identified 91.3% of the screening examinations, whereas the manual method identified 87.8% of them. Both the manual method and NLP correctly identified examinations of patients with adenomas and SSAs in the matched records almost perfectly. Both NLP and the manual method produced comparable values for ADRs for each endoscopist and for the group as a whole.
conclusionsNLP can correctly identify screening colonoscopies, accurately identify adenomas and SSAs in a pathology database, and provide real-time quality metrics for colonoscopy.
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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.