ArticleEndoscopy international open2025
Bowel preparation assessment using artificial intelligence: Systematic review.
Article in Endoscopy international open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
4 citing papers in PubMed.
- EndoClean: A Hybrid Deep Learning Framework for Automated Full-Video Boston Bowel Preparation Scale Assessment.Bioengineering (Basel, Switzerland) · 2026Article
- Colonoscopy Quality and Strategies for Improvement.Gut and liver · 2026Review
- Author reply to letter to the editor: From fragmentation to frameworks: Standardizing AI in gastrointestinal endoscopy.Endoscopy international open · 2025Article
- Urgency for standardized protocols to improve clinical implementation of artificial intelligence in endoscopic diagnostics.Endoscopy international open · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and study aims: Insufficient bowel preparation is the leading cause of missed adenomas in colonoscopy. The Boston Bowel Preparation Scale (BBPS) is the most thoroughly validated and widely used scale to estimate risk of missed adenomas. Artificial intelligence (AI) could automatically quantify bowel preparation, thus reducing bias and limitations inherent in human rating. This systematic review aimed to identify, describe, and evaluate all AI-BPS systems for colonoscopy. Methods: A systematic literature review was conducted using MEDLINE, EMBASE, and SCOPUS based on three sets of terms aligned with the inclusion criteria: colonoscopy, BPS, and AI. Two reviewers independently evaluated and completed data extraction from the articles. Results: A total of 1,449 studies were identified, with eight meeting the eligibility criteria. Six AI-BPS systems were trained on expert BBPS ratings, and two studies used a fecal-mucosal ratio. All studies compared their AI-BPS with expert BBPS ratings; two showed that their AI-BPS outperformed expert BBPS ratings, and six showed comparable performances. Three studies also demonstrated correlations with adenoma detection rates (ADRs), adenoma miss rates (AMRs), or polyp detection rates (PDRs). Only one prospective study implemented its AI-BPS, finding lower AMR in adequately prepared compared with inadequately prepared bowels. Conclusions: AI-BPS can standardize and outperform human bowel preparation evaluation by better correlating with expert BBPS ratings, AMR, ADR, and PDR. Further research following recommended reporting guidelines is needed to allow for cross-study comparisons and meta-analysis, which was not possible in this study due to heterogonous study design and reporting metrics.
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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.