FindTrial
Sign In

Development and Validation of An Artificial Intelligence-assisted System for Predicting Mucosal Invasion of Early Gastric Cancer

Development and Validation of An Artificial Intelligence-Assisted System for Predicting Mucosal Invasion of Early Gastric Cancer

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000031111
Enrollment
Unknown
Registered
2020-03-22
Start date
2020-03-17
Completion date
Unknown
Last updated
2022-01-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Early Gastric Cancer

Interventions

Gold Standard:Pathological results
Index test:Endoscopic&#32
image&#32
collection&#32
of&#32
early&#32
gastric&#32
cancer&#32
and&#32
analysis&#32
artificial&#32
intelligence

Sponsors

Nanjing University Medical School Affiliated Drum Tower Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Patiens aged 18 years or older undergoing gastroscopy. 2. Be able to read, understand and sign informed consent. 3. ASA (American Society of Anesthesiology) risk class 1, 2 or 3.

Exclusion criteria

Exclusion criteria: 1. Patients with absolute contraindications to EGD examination; 2. Patients in pregnancy; 3. A history of previous gastric surgery; 4. Allergic to anaesthetic in previous medical history; 5. The researchers believe that the patient is not suitable to participate in the trial.

Design outcomes

Primary

MeasureTime frame
detection rate of early mucosal (T1a) gastric cancer;SEN, SPE, ACC, AUC of ROC;—

Countries

China

Contacts

Public ContactXiaoping Zou

Nanjing University Medical School Affiliated Drum Tower Hospital

zouxp@nju.edu.cn+86 025-83106666-54588

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026