Clinical trial · Observational
Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals
Development and Validation of Gastroscopy Surveillance Recommendations Based on Natural Language Processing for Patients With Gastric Cancer and Precancerous Diseases
- 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)
The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based follow-up or treatment recommendations
Conditions
Conditions (7)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Atrophic Gastritis | — | UNRESOLVED | — |
| Early Gastric Cancer | Early Gastric Carcinoma | ALIAS | 0.90 |
| Gastric Cancer | Malignant Gastric Neoplasm | CURATED_BROADER | 0.80 |
| Helicobacter Pylori Infection | — | UNRESOLVED | — |
| High Grade Intraepithelial Neoplasia | High Grade Intraepithelial Neoplasia | ONTOLOGY_EXACT | 0.98 |
| Intestinal Metaplasia | — | UNRESOLVED | — |
| Low Grade Intraepithelial Neoplasia | Low Grade Intraepithelial Neoplasia | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI recongnize disease and generate recommendations | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Artificial Intelligence support decision group
- description
- According the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations.
- interventionNames
- Other: AI recongnize disease and generate recommendations
Primary outcomes (2)
- measure
- The diagnostic accuracy of gastric diseases with deep learning algorithm
- timeFrame
- 12 month
- description
- The diagnostic accuracy of gastric diseases with deep learning algorithm
- measure
- The accuracy of recommentions for different disease with deep learning algorithm
- timeFrame
- 12 month
- description
- The accuracy of recommentions for different disease with deep learning algorithm
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: * Patients aged 18 - 80 years * Patients underwent endoscopic examination Exclusion Criteria: * Patients with the contraindications to endoscopic examination * Patients with imcomplete examination information * Patients undergo endoscopy for therapy * Patients have history of upper gastrointestinal surgery * Patients with duodenal or Laryngeal neoplasms * Patients with gastrointestinal submucosal tumor
References
Publications (0)
Data not yet available