Trial reportGut and liver2026
Clinical Efficacy of Real-Time Artificial Intelligence-Assisted Colonoscopy in Colorectal Polyp Detection: A Prospective Multicenter Randomized Controlled Trial.
Trial report in Gut and liver, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/Aims: Early detection and removal of colon polyps are critical for preventing colorectal cancer. Computer-aided detection (CADe) systems have been introduced to increase the polyp detection rate (PDR) during colonoscopy, potentially enhancing its effectiveness. This study aimed to evaluate the efficacy of a CADe system in colorectal neoplasm detection. Methods: This prospective, randomized controlled trial was conducted at two tertiary centers (May 2023 to April 2025). Patients were randomly assigned to CADe or conventional colonoscopy and underwent screening, surveillance, or diagnostic colonoscopy. The primary endpoint was the adenoma detection rate (ADR), while the secondary endpoints were the PDR, relative risk (RR) of polyp detection, adenomas per colonoscopy (APC), and factors influencing adenoma detection. Results: Of 1,004 enrolled patients, 998 were randomly allocated into CADe and conventional colonoscopy groups (497 CADe system and 501 conventional colonoscopy). The CADe group had greater polyp counts (2.2 per colonoscopy vs 1.4 per colonoscopy; p<0.001) and APC values (1.2 vs 0.8; p<0.001). The CADe group showed significantly higher PDRs (72.2% vs 54.5%; p<0.001; RR, 2.173; 95% confidence interval [CI], 1.669 to 2.828) and ADRs (52.3% vs 36.1%; p<0.001; RR, 1.940; 95% CI, 1.505 to 2.499). CADe also significantly increased the detection rate of hyperplastic polyps (p=0.007; RR, 1.474; 95% CI, 1.113 to 1.952) and increased the detection rates across all sizes and locations. In multivariable analysis, CADe use was the strongest independent predictor of adenoma detection (odds ratio, 1.914; 95% CI, 1.467 to 2.496), outweighing male sex, older age, diagnostic indication, and withdrawal time. Conclusions: Real-time CADe-assisted colonoscopy significantly increased PDR and ADR and proved to be a strong independent predictor of adenoma detection (cris.nih.go.kr, KCT0009664).
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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.