Trial reportEndoscopy2024
Linked-color imaging with or without artificial intelligence for adenoma detection: a randomized trial.
Trial report in Endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 4 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Effectiveness of artificial intelligence-assisted colonoscopy for colorectal lesion detection: a systematic review and meta-analysis of randomized controlled trials.BMC medical imaging · 2026Pooled it
- Comparative Performance of Artificial Intelligence-Based Computer-Aided Detection Systems for Colorectal Polyps: A Systematic Review and Network Meta-Analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Pooled it
- Effectiveness of artificial intelligence-assisted colonoscopy in detecting and diagnosing colorectal tumors: a systematic review and network meta-analysis.International journal of colorectal disease · 2025Pooled it
- Optimizing surveillance in Lynch syndrome: lesion detection and comparative performance of different colonoscopy modalities-a systematic review and network meta-analysis.International journal of colorectal disease · 2025Pooled it
- Linked color imaging improves polyp miss rates in total colonoscopy in a multicenter randomized back to back trial.Scientific reports · 2025Trial
- Linked color imaging versus artificial intelligence-assisted linked color imaging for neoplasia detection in the colorectum: a randomized trial in Brazil.Clinical endoscopy · 2026Article
- Randomized Trial of Hood With or Without Wing Attachment-Assisted Colonoscopy With Linked Color Imaging and Computer-Aided in Adenoma Detection.Endoscopy international open · 2026Article
- Computer-aided polyp detection and characterisation systems to support colonoscopy: a systematic review with results stratified by each individual artificial intelligence system.BMJ digital health & AI · 2026Article
- Mixed lesions in premalignant colorectal polyps.Journal of clinical biochemistry and nutrition · 2026Article
- Artificial Intelligence in Colonoscopy: A Systematic Review of Adenoma Versus Polyp Detection Rates.Cureus · 2025Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- AI and Polyp Detection During Colonoscopy.Cancers · 2025Review
- Linked color imaging for the detection and treatment of serrated colon neoplasia: current status and future perspective.Therapeutic advances in gastroenterology · 2025Review
- Concordance with CONSORT-AI guidelines in reporting of randomised controlled trials investigating artificial intelligence in oncology: a systematic review.BMJ oncology · 2025Article
- Review
- Improvement in adenoma detection rate by artificial intelligence-assisted colonoscopy: Multicenter quasi-randomized controlled trial.Endoscopy international open · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdenoma detection rate (ADR) is an important indicator of colonoscopy quality and colorectal cancer incidence. Both linked-color imaging (LCI) with artificial intelligence (LCA) and LCI alone increase adenoma detection during colonoscopy, although it remains unclear whether one modality is superior. This study compared ADR between LCA and LCI alone, including according to endoscopists' experience (experts and trainees) and polyp size.
methodsPatients undergoing colonoscopy for positive fecal immunochemical tests, follow-up of colon polyps, and abdominal symptoms at a single institution were randomly assigned to the LCA or LCI group. ADR, adenoma per colonoscopy (APC), cecal intubation time, withdrawal time, number of adenomas per location, and adenoma size were compared.
resultsThe LCA (n=400) and LCI (n=400) groups showed comparable cecal intubation and withdrawal times. The LCA group showed a significantly higher ADR (58.8% vs. 43.5%; P<0.001) and mean (95%CI) APC (1.31 [1.15 to 1.47] vs. 0.94 [0.80 to 1.07]; P<0.001), particularly in the ascending colon (0.30 [0.24 to 0.36] vs. 0.20 [0.15 to 0.25]; P=0.02). Total number of nonpolypoid-type adenomas was also significantly higher in the LCA group (0.15 [0.09 to 0.20] vs. 0.08 [0.05 to 0.10]; P=0.02). Small polyps (≤5, 6-9mm) were detected significantly more frequently in the LCA group (0.75 [0.64 to 0.86] vs. 0.48 [0.40 to 0.57], P<0.001 and 0.34 [0.26 to 0.41] vs. 0.24 [0.18 to 0.29], P=0.04, respectively). In both groups, ADR was not significantly different between experts and trainees.
conclusionsLCA was significantly superior to LCI alone in terms of ADR.
Indexed as
Identifiers
What OpenQuestion holds
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.