Effective Withdrawal Time and Adenoma Detection Rate

NCT ID: NCT06063720

Last Updated: 2025-09-10

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 193 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-11-01
• Study Completion Date: 2025-01-31

Brief Summary

This study prospectively evaluated the role of EWT versus SWT on adenoma detection rate (ADR) and other key quality metrics. In this prospective single-center study, patients undergoing colonoscopy were enrolled. EWT was calculated in real-time using an AI system with endoscopists blinded to the results. We performed multivariable analyses to assess the association of EWT and SWT with binary (e.g., ADR) and count outcomes (e.g., adenoma per colonoscopy [APC]), after adjusting for patient and procedural characteristics.

Detailed Description

This was a prospective, single-center observational study designed to determine if an AI-powered metric, Effective Withdrawal Time (EWT), is a superior predictor of colonoscopy quality compared to the traditional Standard Withdrawal Time (SWT). All colonoscopies were performed by qualified endoscopists using high-definition white light video scopes. During the procedure, the scope is first advanced to the start of the large intestine (the cecum). The critical examination phase-the withdrawal-begins as the endoscopist slowly pulls the scope back out, meticulously inspecting the colon lining for abnormalities like polyps. It is during this withdrawal that the key metrics were measured. While SWT is a simple duration timed manually, the AI-measured EWT specifically quantifies the time of high-quality mucosal inspection, automatically excluding periods when the camera view is blurry, obscured, or moving too quickly. A crucial aspect of the methodology was that the endoscopists were blinded to the live EWT measurements to prevent the Hawthorne effect, where individuals alter their behaviour because they are being monitored. The study enrolled adults aged 40 and over, excluding patients with conditions that could confound the findings. The primary goal was to assess the independent impact of EWT on the Adenoma Detection Rate (ADR), a key benchmark based on the detection and removal of precancerous polyps for analysis. To achieve this, researchers used multivariable regression models to isolate EWT's effect from other variables and employed correlation tests to statistically compare whether EWT had a stronger relationship with detection quality than SWT

Conditions

Colonic Polyp; Colon Adenoma; Artificial Intelligence

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

AI group

AI monitoring of effective withdrawal time

Endoscreen QC

Intervention Type: DEVICE

Artificial intelligence monitoring of effective withdrawal time

Interventions

Endoscreen QC

Artificial intelligence monitoring of effective withdrawal time

Intervention Type: DEVICE

Eligibility Criteria

Exclusion Criteria

• history of inflammatory bowel disease
• history of colorectal cancer
• previous bowel resection (apart from appendectomy)
• Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
• bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.
• Cecum could not be intubated for various reasons
• Poor bowel preparation with Boston Bowel Preparation Scale (BBPS) < 6

• Minimum Eligible Age: 40 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

The University of Hong Kong

OTHER

Sponsor Role: lead

Responsible Party

Dr. Lui Ka-Luen

Clinical Associate Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Ka Luen Thomas Lui

Role: PRINCIPAL_INVESTIGATOR

The University of Hong Kong

Locations

Queen Mary Hospital, the University of Hong Kong

Hong Kong, , Hong Kong

Countries

Hong Kong

Other Identifiers

AIeffectiveV3

Identifier Type: -

Identifier Source: org_study_id