A study on computer-aided diagnosis method of early gastric neoplasms gastroscopy images based on deep learning

Status

Recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2300074522

Enrollment

Unknown

Registered

2023-08-09

Start date

2023-08-10

Completion date

Unknown

Last updated

2023-08-21

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

Conditions

gastric neoplasms

Interventions

Gold Standard:pathological diagnosis

Index test:White light, NBI, and endoscopic ultrasound with artificial intelligence-assisted endoscopy

Eligibility

Sex/Gender

All

Age

18 Years to 100 Years

Inclusion criteria

Inclusion criteria: ? Adult patients, aged >18 years. ? Endoscopic examination and pathological diagnosis of gastric cancer and non-gastric cancer patients. ? The patients were informed and agreed to participate in this study, and their medical records and follow-up records were complete.

Exclusion criteria

Exclusion criteria: 1. patients with acute bleeding; 2. gastric retention; 3. subjects with a history of previous gastric surgery.

Design outcomes

Primary

Measure	Time frame
The sensitivity of detecting focal lesions;The sensitivity of diagnosing gastric neoplasms;The accuracy of diagnosing gastric neoplasms;The specificity of diagnosing gastric neoplasms;	—

Secondary

Measure	Time frame
The positive predictive value of diagnosing gastric neoplasms;the negative value of diagnosing gastric neoplasms; The false-positive box mean number of detecting gastric abnormities;	—

Countries

China

Contacts

Public ContactXueping Huang

Fujian Provincial Hospital

hxuep@mail2.sysu.edu.cn+86 157 5081 9302

Outcome results

None listed

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