---
title: Artificial Intelligence-assisted System in Colonoscopy
nct_id: NCT06406062
overall_status: RECRUITING
sponsor: Renmin Hospital of Wuhan University
study_type: OBSERVATIONAL
primary_condition: Adenoma Colon Polyp
countries: China
canonical_url: "https://parkinsonspathways.com/agent/trials/NCT06406062.md"
clinicaltrials_gov: "https://clinicaltrials.gov/study/NCT06406062"
ct_last_update_post_date: 2025-04-13
last_seen_at: "2026-05-12T07:00:47.185Z"
source: ClinicalTrials.gov (mirrored, no enrichment)
---
# Artificial Intelligence-assisted System in Colonoscopy

**Official Title:** To Evaluate the Effectiveness and Safety of an Artificial Intelligence-assisted System in Colonoscopy in a Real-world Obsevational Multicenter Study

**NCT ID:** [NCT06406062](https://clinicaltrials.gov/study/NCT06406062)

## Key Facts

- **Status:** RECRUITING
- **Study Type:** OBSERVATIONAL
- **Target Enrollment:** 7500
- **Lead Sponsor:** Renmin Hospital of Wuhan University
- **Conditions:** Adenoma Colon Polyp
- **Start Date:** 2024-05-20
- **Completion Date:** 2025-12-30
- **CT.gov Last Update:** 2025-04-13

## Brief Summary

In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control.

This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control.

This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study.

## Eligibility

- **Minimum age:** 50 Years
- **Sex:** ALL
- **Healthy Volunteers:** No

```
Inclusion Criteria:

1. age \> 50 years old;
2. required diagnostic colonoscopy, screening colonoscopy, or follow-up colonoscopy;
3. voluntarily sign informed consent;
4. Commitment to abide by the study procedures and cooperate with the implementation of the whole process of the study.

Exclusion Criteria:

1. have participated in other clinical trials, signed informed consent and are in the follow-up period of other clinical trials;
2. known polyposis syndrome patients;
3. patients with known IBD;
4. patients considered by the investigators to be unsuitable or unable to undergo complete digestive endoscopy and related examinations;
5. high-risk diseases or other special conditions considered by the investigator to be unsuitable for clinical trial participation.
```

## Arms

- **AI-assisted group** — Group with AI assistance
- **Control group** — Group without AI assistance

## Interventions

- **ANDOANGEL** (DEVICE) — Polyps were identified by endoscopists assisted by an AI system: rectangular box marks; Monitoring of ileocecal position: whether blindness was reached was displayed in the lower left corner of the interface.

Mirror entry and exit time monitoring: the operation time is displayed in the upper left corner of the interface.

Colonoscopy withdrawal speed monitoring: the relative withdrawal speed was displayed on the left side of the interface.

## Primary Outcomes

- **Adenoma detection rate** _(time frame: During Endoscopy procesure)_ — The numerator is the number of patients who had at least one adenoma on colonoscopy, and the denominator is the total number of patients who underwent colonoscopy

## Secondary Outcomes

- **Polyp detection rate** _(time frame: During Endoscopy procesure)_
- **Detection rate of serrated adenoma** _(time frame: During Endoscopy procesure)_
- **Average number of polyps per colonoscopy** _(time frame: During Endoscopy procesure)_
- **Colonoscopy time** _(time frame: During Endoscopy procesure)_
- **Proportion of over-speed frames** _(time frame: During Endoscopy procesure)_

## Locations (1)

- Renmin Hospital of Wuhan Univercity, Wuhan, Hubei, China — _RECRUITING_

## Recent Field Changes (last 30 days)

- `design.enrollmentCount` — added _(2026-05-12)_
- `status.overallStatus` — added _(2026-05-12)_
- `status.primaryCompletionDate` — added _(2026-05-12)_
- `status.completionDate` — added _(2026-05-12)_
- `status.lastUpdatePostDate` — added _(2026-05-12)_
- `eligibility.criteria` — added _(2026-05-12)_
- `eligibility.minAge` — added _(2026-05-12)_
- `eligibility.sex` — added _(2026-05-12)_
- `outcomes.primary` — added _(2026-05-12)_
- `outcomes.secondary` — added _(2026-05-12)_
- `armsInterventions.arms` — added _(2026-05-12)_
- `armsInterventions.interventions` — added _(2026-05-12)_
- `sponsor.lead` — added _(2026-05-12)_
- `results.hasResults` — added _(2026-05-12)_
- `locations.renmin hospital of wuhan univercity|wuhan|hubei|china` — added _(2026-05-12)_

---

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