Evidence map›Paper›PMID 39281103›Full record

ArticleEClinicalMedicine2024

Efficacy of a real-time intelligent quality-control system for the detection of early upper gastrointestinal neoplasms: a multicentre, single-blinded, randomised controlled trial.

Ruchen Zhou, Jing Liu, Chenchen Zhang, Yusha Zhao, Jingran Su, Qiong Niu, Chengxia Liu, Zhuang Guo, Zhenqin Cui, Xiaoqin Zhong and 12 more

Registry-linked trialAbstract read
In one paragraph

Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04720924 (Computer-aided Real-time Automatic Quality-control System for Detection of Early Cancer and Precancerous Lesions on Upper Gastrointestinal Tract), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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.

NCT04720924 nacompletednot on this map

Computer-aided Real-time Automatic Quality-control System for Detection of Early Cancer and Precancerous Lesions on Upper Gastrointestinal Tract: a Multicenter Randomized Controlled Study

TypeinterventionalSponsorShandong UniversityRan2021 to 2023Enrolled1,840ConditionsEsophagogastroduodenoscopyArmsComputer-aided Real-time Automatic Quality-control System
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Screening strategies for the early detection of non-cardia gastric cancer in low- to moderate-prevalence regions: Asia-Pacific gastroesophageal cancer congress consensus 2026.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
    Review
  4. Review
  5. Artificial Intelligence for Endoscopic Diagnosis of Gastric Premalignant Lesions.The Korean journal of helicobacter and upper gastrointestinal research · 2026
    Review
  6. Article
  7. Article
  8. World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026
    Review
  9. Review
  10. Article
  11. Review
  12. The ACPGBI AI taskforce report: A mixed-methods roadmap for AI in colorectal surgery.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2025
    Article
  13. Article
  14. Review
  15. Review
  16. Article
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

22 authors.

Ruchen ZhouDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Jing LiuDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Chenchen ZhangDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Yusha ZhaoDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Jingran SuDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Qiong NiuDepartment of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Chengxia LiuDepartment of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Zhuang GuoCentral Hospital of Shengli Oilfield, Dongying, Shandong, China.
Zhenqin CuiCentral Hospital of Shengli Oilfield, Dongying, Shandong, China.
Xiaoqin ZhongZibo Municipal Hospital, Zibo, Shandong, China.
Weidong ZhaoZibo Municipal Hospital, Zibo, Shandong, China.
Jing LiLinyi People's Hospital, Dezhou, Shandong, China.
Xiaodong ZhangLinyi People's Hospital, Dezhou, Shandong, China.
Hongyan WangPKUCare Luzhong Hospital, Zibo, Shandong, China.
Shidong SunPKUCare Luzhong Hospital, Zibo, Shandong, China.
Ruiguang MaDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Xinyu ChenDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Xinyan XuDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Yiqing ZhuDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Zhen LiDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Xiuli ZuoDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Yanqing LiDepartment of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Oesophagogastroduodenoscopy (OGD) quality and identification of the early upper gastrointestinal (UGI) neoplasm play an important role in detecting the UGI neoplasm. However, the optimal method for quality control in daily OGD procedures is currently lacking. We aimed to evaluate the efficacy of a real-time intelligent quality-control system (IQCS), which combines OGD quality control with lesion detection of early UGI neoplasms. Methods: We performed a multicentre, single-blinded, randomised controlled trial at 6 hospitals in China. Patients aged 40-80 years old who underwent painless OGD were screened for enrolment in this study. Patients with a history of advanced UGI cancer, stenosis, or obstruction in UGI tract were excluded. Eligible subjects were randomly assigned (1:1) to either the routine or IQCS group to undergo standard OGD examination and OGD examination aided by IQCS, respectively. Patients were masked to the randomisation status. The primary outcome was the detection of early UGI neoplasms. All analyses were done on a per-protocol basis. This trial is registered with ClinicalTrials.gov, NCT04720924. Findings: Between January 16, 2021 and December 23, 2022, 1840 patients were randomised (IQCS group: 919, routine group: 921). The full analysis set consisted of 914 in the IQCS group and 915 in the routine group. The early UGI neoplasms detection rate in the IQCS group (6.1%, 56/914) was significantly higher than in the routine group (2.3%, 21/915; Interpretation: The IQCS improved the OGD quality and increased early UGI neoplasm detection in different hospital types and endoscopist experiences. IQCS could play an important role in primary basic hospitals and non-expert endoscopists to improve the diagnostic accuracy of early UGI neoplasms. The effectiveness of IQCS in real-world clinical settings needs a larger population validation. Funding: Key R&D Program of Shandong Province, China (Major Scientific and Technological Innovation Project), National Natural Science Foundation of China, the Taishan Scholars Program of Shandong Province, the National Key Research and Development Program of China, and the Shandong Provincial Natural Science Foundation.

Indexed as

Artificial intelligenceEarly gastric cancerGastrointestinal neoplasmsOesophagogastroduodenoscopyQuality control

Identifiers

PMID39281103
PMCPMC11402435

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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.