Evidence map›Paper›PMID 41943654›Full record

ArticleClinical endoscopy2026

Linked color imaging versus artificial intelligence-assisted linked color imaging for neoplasia detection in the colorectum: a randomized trial in Brazil.

Carlos Eduardo Oliveira Dos Santos, Naohisa Yoshida, Asadur Jorge Tchekmedyian, Gabriel Malaman Dos Santos, Luma Alves Costa, Ivan David Arciniegas Sanmartin, Júlio Pereira-Lima

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Article in Clinical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Carlos Eduardo Oliveira Dos SantosDepartment of Endoscopy, Santa Casa de Caridade Hospital, Bagé, Brazil.
Naohisa YoshidaDepartment of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Asadur Jorge TchekmedyianDepartment of Endoscopy, Asociación Española, Montevideo, Uruguay.
Gabriel Malaman Dos SantosDepartment of Endoscopy, Santa Casa de Caridade Hospital, Bagé, Brazil.
Luma Alves CostaDepartment of Endoscopy, Santa Casa de Caridade Hospital, Bagé, Brazil.
Ivan David Arciniegas SanmartinDepartment of Gastroenterology and Endoscopy, Mãe de Deus Hospital, Porto Alegre, Brazil.
Júlio Pereira-LimaDepartment of Gastroenterology and Endoscopy, Santa Casa Hospital, Porto Alegre, RS, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdenomas and sessile serrated lesions (SSLs) are neoplasms that play a role in colorectal cancer development. Improving adenoma detection rate (ADR) and SSL detection rates (SDR) allows for more effective prevention of colorectal cancer. Linked color imaging (LCI) and artificial intelligence (AI) have contributed to increased ADR and SDR. This study aimed to compare the neoplasia detection rates (NDR, adenomas, or SSLs) between LCI and AI-assisted LCI colonoscopy.

methodsWe conducted a prospective randomized trial to compare LCI with LCI+AI. We evaluated ADR, SDR, and NDR as the primary outcomes.

resultsA total of 779 polyps were detected in 622 patients (304 in the LCI group and 318 in the LCI+AI group); 555 were adenomas and 62 were SSLs for a total of 617 neoplastic lesions (79.2%) in 363 patients. Comparing the LCI and LCI+AI groups, the ADR, SDR, and NDR were 46.7% vs. 53.1% (p=0.13), 8.2% vs. 8.5% (p=0.90), and 52.3% vs. 56.6% (p=0.30), respectively. The mean number of adenomas per patient, advanced ADR, and withdrawal time did not significantly differ between the two groups.

conclusionsSimilar results were observed in both groups, and even with the good performance of AI-assisted LCI, LCI alone yielded high ADR, SDR, and NDR.

Indexed as

AdenomaArtificial intelligenceColon neoplasmsColonoscopyLinked color imaging

Identifiers

PMID41943654
PMCPMC13054503

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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.