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Study on Real-time Detection and Tracking System of Early Gastric Cancer Based on Lightweight Deep Learning Network

Study on Real-time Detection and Tracking System of Early Gastric Cancer Based on Lightweight Deep Learning Network

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200056324
Enrollment
Unknown
Registered
2022-02-03
Start date
2022-02-03
Completion date
Unknown
Last updated
2024-09-23

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

Conditions

Early-stage gastric cancer

Interventions

Gold Standard:Endoscopic images and pathological confirmation
Index test:Endoscoic Image of early gastric cancer and confirmation of endoscopist

Sponsors

The First Affiliated Hospital of Anhui Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Aged 18 to 80 years; 2. Endoscopic diagnosis of patients with early gastric cancer, and the pathology confirmed to be early gastric cancer; 3. The picture has a high definition, which can clearly identify the type of lesion; 4. The Hospital Ethics Committee has approved the research content.

Exclusion criteria

Exclusion criteria: 1. Pictures of suspected early cancer, the pathology confirmed to be inflammatory or progressive cancer; 2. Fuzzy pictures, poor spotlight / clarity, affecting diagnosis.

Design outcomes

Primary

MeasureTime frame
Age;Location of gastric cancer;Type of gastric cancer;—

Countries

China

Contacts

Public ContactKong Derun

The First Affiliated Hospital of Anhui Medical University

kongderun168@163.com+86 13955155476

Outcome results

None listed

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