Effective Withdrawal Time and Adenoma Detection Rate

Prospective Evaluation of Artificial Intelligence-assisted Monitoring of Effective Withdrawal Time on Adenoma Detection

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.

Study Overview

• Status: Completed
• Conditions: Colonic Polyp; Artificial Intelligence; Colon Adenoma
• Intervention / Treatment: Device: Endoscreen QC

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

• Study Type: Observational
• Enrollment (Actual): 193

Contacts and Locations

Study Locations

• Hong Kong
  • Hong Kong, Hong Kong
    • Queen Mary Hospital, The University of Hong Kong

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

All patient will be recruited in Queen Mary Hospital, Hong Kong, China

Description

Inclusion Criteria: All adult patients, aged 40 or above, undergoing outpatient colonoscopy will be recruited

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
AI group; AI monitoring of effective withdrawal time	Device: Endoscreen QC; Artificial intelligence monitoring of effective withdrawal time

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Adenoma detection rates	Adenoma detection rates	During the colonoscopy

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Polyp detection rate	Polyp detection rates of the colonoscopy	During that colonoscopy
Adenoma per colonoscopy	Adenoma per colonoscopy	During colonoscopy
Polyp per colonoscopy	Polyp per colonoscopy	During colonoscopy
Serrated lesion detection rate	Serrated lesion detection rates of that colonoscopy	During colonoscopy
Advanced adenoma detection rate	Advanced adenoma detection rates of that colonoscopy	During colonoscopy
Serrated lesion per colonoscopy	Serrated lesion per colonoscopy	During colonoscopy
Advanced adenoma per colonoscopy	Advanced adenoma per colonoscopy	During colonoscopy

Collaborators and Investigators

• Sponsor: The University of Hong Kong
• Investigators: Principal Investigator: Ka Luen Thomas Lui, The University of Hong Kong

Study record dates

Study Major Dates

• Study Start (Actual): November 1, 2023
• Primary Completion (Actual): September 30, 2024
• Study Completion (Actual): January 31, 2025

Study Registration Dates

• First Submitted: September 20, 2023
• First Submitted That Met QC Criteria: September 30, 2023
• First Posted (Actual): October 2, 2023

Study Record Updates

• Last Update Posted (Estimated): September 10, 2025
• Last Update Submitted That Met QC Criteria: September 3, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: Artificial intelligence; Colonic Polyp; Withdrawal time
• Additional Relevant MeSH Terms: Pathological Conditions, Anatomical; Polyps; Intestinal Polyps; Pathological Conditions, Signs and Symptoms; Colonic Polyps
• Other Study ID Numbers: AIeffectiveV3

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No