NCT04563416 is a clinical trial in Artificial Intelligence, sponsored by Shandong University.

Who is sponsoring NCT04563416?

NCT04563416 is sponsored by Shandong University.

What condition does NCT04563416 study?

NCT04563416 studies Artificial Intelligence, Optical Enhancement Endoscopy, Magnifying Endoscopy.

Is NCT04563416 still recruiting?

NCT04563416 is currently status unknown. Last updated 24 September 2020.

Last reviewed 2020-09-24 · How we verify

NCT04563416

Name: Drug Landscape Pharmaceutical Database
Creator: Drug Landscape
Published: 2026-01-01
License: https://creativecommons.org/licenses/by/4.0/

Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy

Status unknown Last updated 24 September 2020

What this trial tests

trial in Artificial Intelligence in 80 participants. Status unknown.

Timeline

10 July 2020

Primary endpoint
30 November 2020

30 December 2020

Quick facts

Lead sponsor	Shandong University
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	80
Start date	10 July 2020
Primary completion	30 November 2020
Estimated completion	30 December 2020
Sites	1 location across China

Conditions studied

Artificial Intelligence — all drugs for Artificial Intelligence →
Optical Enhancement Endoscopy — all drugs for Optical Enhancement Endoscopy →
Magnifying Endoscopy — all drugs for Magnifying Endoscopy →

Sponsor

Shandong University

Who can join

18 and older, any sex, with Artificial Intelligence or Optical Enhancement Endoscopy. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos.
Ma M, Li Z, Yu T, Liu G, et al · · 2022 · cited 1× · PMID 35992850 · DOI 10.3389/fonc.2022.945904

Verify or expand the search:

Other recruiting trials for Artificial Intelligence

Currently open trials in the same condition.

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Other Shandong University trials

Trials by the same sponsor.

NCT07288346 — Effect of Predictive Model on Double-Balloon Enteroscopy · NA · recruiting
NCT07325344 — Active Locomotion of Magnetically Controlled Capsule Endoscopy in the Small Bowel: A Feasibility Study · NA · recruiting
NCT07319312 — Evaluating the Effect of Nurse-Led Stroke Transitional Care in Tanzania · recruiting
NCT07391787 — Low-acid Contrast Media for Preventing Post ERCP Pancreatitis · completed
NCT07066059 — Study on the Efficacy and Safety of Shouhui Tongbian Capsules in Protecting Intestine During the Perioperative Period · EARLY_PHASE1 · not yet recruiting

Verify against primary sources

ClinicalTrials.gov — authoritative US registry record
WHO ICTRP — international registry index
EU Clinical Trials Register
Sponsor press releases (Google)

Data sources for this page

Trial protocol + status: ClinicalTrials.gov NCT04563416 (US National Library of Medicine, public domain)
Publications: Europe PMC API search by NCT ID, retrieved 10 June 2026
Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
Sponsor: as reported to ClinicalTrials.gov by Shandong University
Last refreshed: 24 September 2020

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT04563416.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing