Introduction

Bowel preparation quality is a key determinant of colonoscopy effectiveness and adenoma detection^1,2. Manual evaluation of free-text colonoscopy reports is labor-intensive and limits quality monitoring³. Developing a natural language processing (NLP) application for measuring colonoscopy quality demonstrates the potential to automate this process by extracting key quality indicators⁴.

Aims & Methods

We aimed to develop and validate a NLP approach to automatically classify bowel cleanliness and to comprehensively structure colonoscopy reports by extracting procedural and pathological variables. A total of 11,374 reports from our institutional endoscopy unit were analyzed. An annotated dataset of 1,018 reports labeled as “good,” “intermediate,” or “poor” preparation was used to train a TF-IDF–based logistic regression classifier. Cross-validation and test performance were evaluated using accuracy, precision, recall, and F1-scores, and the model was applied to the full dataset. Additional variables (cecal intubation, polyp presence, number, size, localization, polypectomy method, and hemostasis application) were extracted using rule-based NLP heuristics. Adenoma detection rate (ADR) was calculated in a predefined subset (January 2023–July 2025) with complete pathology data, and multivariable logistic regression identified independent predictors.

Results

The NLP classifier achieved a cross-validated accuracy of 92%. Precision and recall were highest for “good” preparation (0.98 and 0.99) and acceptable for “intermediate” (0.85 and 0.82) and “poor” (0.85 and 0.87). Application to the full cohort yielded 5,787 (50.9%) good, 2,176 (19.1%) intermediate, and 3,411 (30.0%) poor preparations. Leveraging these structured data, ADR analysis in the subset of 5,420 patients showed an overall rate of 20.4%. Multivariable analysis demonstrated that ADR was independently associated with older age (OR 1.05 per year, 95% CI 1.05–1.06, p<0.001), male sex (OR 1.74, 95% CI 1.51–2.01, p<0.001), adequate bowel preparation (OR 1.46, 95% CI 1.21–1.75, p<0.001), cecal intubation (OR 1.76, 95% CI 1.43–2.16, p<0.001), and procedures performed by specialists compared with trainees (24.0% vs 20.3%; OR 1.25, 95% CI 1.05–1.47, p=0.010).

Conclusion

This large-scale real-world analysis demonstrates the feasibility of NLP-based automated classification of bowel preparation quality and its direct utility in generating structured colonoscopy datasets for quality monitoring. By integrating bowel cleanliness with other performance indicators (age, sex, cecal intubation, operator expertise), our framework not only achieved high classification accuracy but also provided clinically relevant benchmarks for ADR, supporting targeted quality improvement in endoscopy units.

References

1. Clark BT, Protiva P, Nagar A, et al. Quantification of Adequate Bowel Preparation for Screening or Surveillance Colonoscopy in Men. Gastroenterology. 2016;150(2):396-e15. doi:10.1053/j.gastro.2015.09.041
2. Clark BT, Rustagi T, Laine L. What level of bowel prep quality requires early repeat colonoscopy: systematic review and meta-analysis of the impact of preparation quality on adenoma detection rate. Am J Gastroenterol. 2014;109(11):1714-1724. doi:10.1038/ajg.2014.232
3. Raju GS, Lum PJ, Slack RS, et al. Natural language processing as an alternative to manual reporting of colonoscopy quality metrics. Gastrointest Endosc. 2015;82(3):512-519. doi:10.1016/j.gie.2015.01.049
4. Harkema H, Chapman WW, Saul M, Dellon ES, Schoen RE, Mehrotra A. Developing a natural language processing application for measuring the quality of colonoscopy procedures. J Am Med Inform Assoc. 2011;18 Suppl 1(Suppl 1):i150-i156. doi:10.1136/amiajnl-2011-000431

Introduction

Irritable Bowel Syndrome (IBS) is a prevalent and chronic gastrointestinal disorder that affects approximately 85 million Europeans. It predominantly impacts women and significantly impairs quality of life, presenting with recurrent abdominal pain and altered bowel habits in the absence of identifiable structural abnormalities. Beyond its gastrointestinal symptoms, IBS frequently co-occurs with mental and somatic conditions such as anxiety, depression, fibromyalgia (32.5%), and chronic fatigue syndrome (14%). Up to 60% of IBS patients experience mental comorbidities, highlighting the complex interplay between gut and brain—often referred to as dysregulation of the gut–brain axis. This dysregulation manifests in altered intestinal motility, increased gut permeability, visceral hypersensitivity, and significant shifts in gut microbiota composition.

Aims & Methods

We present the DISCOVERIE project (Development, dIagnostic and prevention of gender-related Somatic and mental COmorbitiEs in iRritable bowel syndrome In Europe), an international, multi-centre initiative aimed at characterizing IBS and its associated comorbidities, with a particular focus on sex- and gender-related differences. To this end, we generated a comprehensive multi-omics dataset from 743 individuals, including shotgun quantitative microbiome profiling and untargeted metabolomics. These molecular data were complemented by in-depth clinical phenotyping, capturing information on anxiety, depression, fibromyalgia, and chronic fatigue syndrome. Particular attention was given to age, sex/gender, lifestyle factors, and systemic inflammatory markers. Multivariate statistical models were applied, correcting for common microbiome confounders, to analyze the multi-omics layers and identify microbiome-related signatures and mechanistic links across IBS and its comorbid subgroups.

Results

Our microbiome analysis revealed that IBS subjects exhibit features of a dysbiotic bacterial community, characterized by an increased prevalence of the Bacteroides 2 enterotype (17–26% in IBS participants across comorbid groups vs. 17% in the general population and only 2% in the healthy control group; chi-squared test, FDR < 0.05), a reduced butyrate synthesis capacity, and increased levels of opportunistic taxa compared to non-IBS controls.
Interestingly, IBS individuals—regardless of comorbidities—showed enrichment of bacterial metabolic modules involved in the degradation of amino acids that serve as precursors for neurotransmitters. Specifically, we observed an increase in pathways associated with GABA degradation and a decrease in modules responsible for glutamate biosynthesis. This dysregulation of gut neurotransmitter metabolism may contribute to hallmark IBS symptoms.
Strikingly, pro-inflammatory IL-6 levels were strongly associated with specific bacterial genus (FDR < 0.05), as well as with a metabolic module responsible for the degradation of gamma-hydroxybutyric acid (GHB), a neurotransmitter and central nervous system depressant, suggesting a potential mechanistic link between low-grade inflammation and depressive symptoms in IBS.

Conclusion

The results from this large-scale, multi-center international study reveal a dysregulation of gut neurotransmitter metabolism that may contribute to IBS symptoms and associated comorbidities. These findings underscore a potential link between elevated IL-6 levels, microbial metabolism, and the gut–brain axis, offering new insights into the complex interplay between microbiota composition, host physiology, and IBS-related comorbidities.