Research for the Identification and Tracking of Colon Lesions Based on Artificial Intelligence Deep Learning Algorithms

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2000028906

Enrollment

Unknown

Registered

2020-01-07

Start date

2020-01-09

Completion date

Unknown

Last updated

2020-01-13

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

Conditions

Colon polyps, etc.

Interventions

Gold Standard:Pathological examination

Index test:real-time&#32

endoscopic&#32

recognition&#32

system

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: (1) Aged 18-75 years; (2) Patients undergoing gastroscopy during clinical diagnosis and treatment; (3) Sign a written informed consent.

Exclusion criteria

Exclusion criteria: 1. Patients undergoing lower gastrointestinal emergency colonoscopy due to blood in the stool and abdominal pain; 2. History of other malignant tumor diseases; 3. pregnancy and lactation; 4. Patients cannot understand the purpose of this study

Design outcomes

Primary

Measure	Time frame
Colonic disease detection rate;Colon detection accuracy;Colon lesion tracking accuracy;	—

Countries

China

Contacts

Public ContactHuang Jin

Digestive Endoscopy Center, 988th Hospital, Joint Logistics Support Force

419043875@qq.com+86 13673366093

Outcome results

None listed

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