Clinical trial · Observational
Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology
NCT04384575CI-TRIAL-00071788completedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Gastric Cancer | Malignant Gastric Neoplasm | CURATED_BROADER | 0.80 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| blind spots | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (3)
- measure
- Accuracy
- timeFrame
- 2020.2.22-2020.7.1
- description
- Calculate the accuracy of AI's judgment on images
- measure
- Sensitivity
- timeFrame
- 2020.2.22-2020.7.1
- description
- number of images in which AI correctly diagnosed positive/all images with positive
- measure
- Specificity
- timeFrame
- 2020.2.22-2020.7.1
- description
- number of images in which AI correctly diagnosed negative/all images negative
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: 1. Patiens aged 18 years or above undergoing gastroscopy； 2. Be able to read, understand and sign informed consent； Exclusion Criteria: 1. Patients with absolute contraindications to endoscopy examination; 2. pregnant women; 3. previous history of gastric surgery; 4. the researcher considers that the subject is not suitable for clinical trial.
References
Publications (1)
- DERIVEDYuan P, Ma ZH, Yan Y, Li SJ, Wang J, Wu Q. Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images. Int J Gen Med. 2024 Dec 12;17:6127-6138. doi: 10.2147/IJGM.S481127. eCollection 2024. PMID 39691834