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Related Concept Videos

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Effect of a Computer-Aided Device for Detecting Gastric Neoplasms: A Multicenter, Randomized Controlled Trial.

Zehua Dong1, Lianlian Wu1, Hongliu Du1

  • 1Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease, Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Engineering Research Center for Artificial Intelligence Endoscopy Interventional Treatment of Hubei Province, Wuhan, China.

Gastroenterology
|March 9, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) did not improve gastric neoplasm detection rates in upper endoscopy. However, AI reduced blind spots and showed potential benefits for less experienced endoscopists.

Keywords:
Artificial IntelligenceEsophagogastroduodenoscopyGastric Neoplasms

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Area of Science:

  • Gastroenterology
  • Medical Artificial Intelligence
  • Clinical Trials

Background:

  • Multicenter randomized controlled trials on artificial intelligence (AI) in upper endoscopy are limited.
  • The efficacy of AI in enhancing gastric neoplasm detection requires further investigation.

Purpose of the Study:

  • To evaluate the impact of AI-assisted upper endoscopy on gastric neoplasm detection rates.
  • To assess secondary outcomes including early gastric cancer detection and procedural efficiency.

Main Methods:

  • A multicenter randomized controlled trial involving 29,514 patients comparing AI-assisted versus non-assisted esophagogastroduodenoscopy.
  • Primary outcome: detection rate of gastric neoplasms post-pathologic review. Secondary outcomes: detection rates before review, early gastric cancer ratio, metaplasia/atrophy detection, biopsy rates, blind spots, and procedure time.
  • Intention-to-treat (ITT), per-protocol, and subgroup analyses were performed.

Main Results:

  • AI did not significantly improve the overall detection rate of gastric neoplasms after pathologic review (1.42% vs 1.25%, P = .25).
  • AI improved detection rates based on original pathology (4.06% vs 3.57%, P = .03) and significantly reduced blind spots (2.52 to 1.07, P < .001), while increasing procedure time.
  • Subgroup analysis indicated potential benefits for less experienced endoscopists and during fatigue periods. AI demonstrated high diagnostic accuracy for gastric adenocarcinoma and intraepithelial neoplasia.

Conclusions:

  • AI did not enhance the overall detection rate of gastric neoplasms in this large multicenter trial.
  • AI shows promise in reducing blind spots and may assist less experienced endoscopists, but further real-world studies are needed to confirm its adaptability.
  • The study highlights the need for continued research into AI's role in improving endoscopic diagnostics.