Evaluation of Artificial Intelligence System in Diagnosis of Colorectal Tubular Adenoma Lesions

Recruiting

• Conditions: Colorectal Adenoma; Artificial Intelligence (AI) in Diagnosis

• Registration Number: NCT07073430
• Lead Sponsor: Renmin Hospital of Wuhan University

Brief Summary

This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-11-01
• Status Verified: 2025-06
• Study First Submitted: 2025-07-09
• Study First Submitted QC: 2025-07-09
• Last Update Submitted: 2025-07-09
• Study First Posted: 2025-07-18
• Last Update Posted: 2025-07-18
• Primary Completion: 2026-10-31
• Study Completion: 2026-10-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 4200

Inclusion Criteria

• Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender.
• Voluntarily sign the informed consent form
• Promise to abide by the research procedures and cooperate in the implementation of the entire research process.

Exclusion Criteria

• Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past;
• Patients who has definite active lower gastrointestinal bleeding.
• Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease;
• Uncontrolled hypertension (systolic blood pressure > 160 mmHg or diastolic blood pressure > 95 mmHg after standardized treatment)
• There is a history of stroke, coronary artery disease, or vascular disease;
• Pregnant;
• Intestinal preparation cannot be carried out.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The accuracy rate of diagnosing adenomas	during endoscopy	The accuracy rate of the endoscopic AI model in diagnosing adenomas (presence or absence of adenomas, number of adenomas, advanced adenomas).

Secondary Outcome Measures

Name	Time	Method
The prediction for the disease risk level	during endoscopy	The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.

Trial Locations

Locations (1)

Renmin Hospital of Wuhan University; 🇨🇳 Wuhan, Hubei, China