Assessing the effectiveness of texture and color enhancement imaging versus white‐light endoscopy in detecting gastrointestinal lesions: A systematic review and meta‐analysis
Bibliographic record
Abstract
Introduction: Gastrointestinal cancers account for 26% of cancer incidence and 35% of cancer-related deaths globally. Early detection is crucial but often limited by white light endoscopy (WLE), which misses subtle lesions. Texture and color enhancement imaging (TXI), introduced in 2020, enhances texture, brightness, and color, addressing WLE's limitations. This meta-analysis evaluates TXI's effectiveness compared to WLE in gastrointestinal lesion lesion detection. Methods: A systematic review and meta-analysis were conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Searches of CENTRAL, PubMed, Embase, and Web of Science identified randomized controlled trials and observational studies comparing TXI with WLE. Outcomes included lesion detection rates, color differentiation, and visibility scores. The risk of bias was assessed using the Cochrane ROB 2.0 tool and Newcastle-Ottawa tools, and evidence certainty was evaluated using Grading of Recommendations Assessment, Development, and Evaluation. Results: Seventeen studies with 16,634 participants were included. TXI significantly improved color differentiation (mean difference: 3.31, 95% confidence interval [CI]: 2.49-4.13), visibility scores (mean difference: 0.50, 95% CI: 0.36-0.64), and lesion detection rates (odds ratio [OR]: 1.84, 95% CI: 1.52-2.22) compared to WLE. Subgroup analyses confirmed TXI's advantages across pharyngeal, esophageal, gastric, and colorectal lesions. TXI also enhanced adenoma detection rates (OR: 1.66, 95% CI: 1.31-2.12) and mean adenoma detection per procedure (mean difference: 0.48, 95% CI: 0.25-0.70). Conclusion: TXI improves gastriontestinal lesion lesion detection by enhancing visualization and color differentiation, addressing key limitations of WLE. These findings support its integration into routine endoscopy, with further research needed to compare TXI with other modalities and explore its potential in real-time lesion detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.046 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".