Trial reportGut2019
Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy.
Trial report in Gut, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 132 papers, 11 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.
Real-Time Identification of Gastric Lesions and Anatomical Landmarks During Magnetically Controlled Capsule Endoscopy Using Artificial Intelligence
Effect of Artificial Intelligence Monitoring Blind Spots of EGD on the Inspection Time and Dection Rate of Neoplastic Lesions
Who cites it
132 citing papers in PubMed, 11 syntheses or guidelines pooled it, 309 citations in OpenAlex.
- Novel endoscopic techniques for the diagnosis of gastricFrontiers in microbiology · 2024Pooled it
- Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review.JAMA network open · 2022Pooled it
- Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review.Journal of medical Internet research · 2022Pooled it
- The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study.Journal of medical Internet research · 2022Pooled it
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- State of the Art: The Impact of Artificial Intelligence in Endoscopy 2020.Current gastroenterology reports · 2021Pooled it
- Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis.Frontiers in medicine · 2021Pooled it
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension.Nature medicine · 2020Guideline
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.Nature medicine · 2020Guideline
- Is Routine Preoperative Esophagogastroduodenoscopy Prior to Bariatric Surgery Mandatory? Systematic Review and Meta-analysis of 10,685 Patients.Obesity surgery · 2020Pooled it
- Deep learning in gastric tissue diseases: a systematic review.BMJ open gastroenterology · 2020Pooled it
- Technical and clinical feasibility of single-use gastroscopy with real-time AI-based quality monitoring and single-use colonoscopy: a prospective two-center study.Biomedical engineering online · 2026Trial
- Impact of a real-time automatic quality control system for magnetically controlled capsule gastroscopy: a multicenter randomized controlled trial.BMC medicine · 2026Trial
- Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.Epilepsia · 2023Trial
- Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial.BMC anesthesiology · 2022Trial
- 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 · 2026Review
- Artificial Intelligence for Esophageal Precancerous Lesions and Esophageal Cancer.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Artificial Intelligence for Endoscopic Diagnosis of Gastric Premalignant Lesions.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Clinical Performance of Gastric CADe: Beyond Lesion Recognition From the G-CADe Trial.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Article
- Application of Artificial Intelligence in the Endoscopic Diagnosis of Gastric Cancer and Precancerous Lesions.Diagnostics (Basel, Switzerland) · 2026Review
72 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
29 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEsophagogastroduodenoscopy (EGD) is the pivotal procedure in the diagnosis of upper gastrointestinal lesions. However, there are significant variations in EGD performance among endoscopists, impairing the discovery rate of gastric cancers and precursor lesions. The aim of this study was to construct a real-time quality improving system, WISENSE, to monitor blind spots, time the procedure and automatically generate photodocumentation during EGD and thus raise the quality of everyday endoscopy.
designWISENSE system was developed using the methods of deep convolutional neural networks and deep reinforcement learning. Patients referred because of health examination, symptoms, surveillance were recruited from Renmin hospital of Wuhan University. Enrolled patients were randomly assigned to groups that underwent EGD with or without the assistance of WISENSE. The primary end point was to ascertain if there was a difference in the rate of blind spots between WISENSE-assisted group and the control group.
resultsWISENSE monitored blind spots with an accuracy of 90.40% in real EGD videos. A total of 324 patients were recruited and randomised. 153 and 150 patients were analysed in the WISENSE and control group, respectively. Blind spot rate was lower in WISENSE group compared with the control (5.86% vs 22.46%, p<0.001), and the mean difference was -15.39% (95% CI -19.23 to -11.54). There was no significant adverse event.
conclusionsWISENSE significantly reduced blind spot rate of EGD procedure and could be used to improve the quality of everyday endoscopy. TRIAL REGISTRATION NUMBER: ChiCTR1800014809; Results.
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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.