ClinicalTrials.gg
Status unknownNCT03908645Updated Apr 9, 2019

Development and Validation of a Deep Learning Algorithm for Bowel Preparation Quality Scoring

An interventional study of Artificial intelligence assisted bowel preparation quality scoring system and Conventional human scoring in Bowel Preparation, sponsored by Shandong University. Status unknown at 1 site in China. Open to participants aged 18 Years to 70 Years. Per ClinicalTrials.gov, last updated 2019-04-09.

Sponsored by Shandong University · Not applicable, Interventional, and Health services research

The sponsor has not verified this record recently (last verified Apr 2019), so the status shown — last known as Active, not recruiting — may be out of date.

From the registry’s dates

  • Registered 3 months after the study started (first participant enrolled Dec 2018, registered Apr 2019).
Phase
Not applicable
Study type
Interventional
Enrollment
100
Allocation
Randomized
Ages
18 Years to 70 Years
Sex
All
01

Study summary

The purpose of this study is to develop and validate the performance of an artificial intelligence(AI) assisted Boston Bowel preparation Scoring(BBPS) system for evaluation of bowel cleanness, then testify whether this new scoring system can help physicians to improve the quality control parameters of colonoscopy in clinic practice.

Read the detailed description

Colonoscopy is recommended as a routine examination for colorectal cancer screening. Adequate bowel preparation is indispensable to ensure a clear vision of colonic mucosa,complete inspection of all colon segments, and furthermore improves the detection rates of small adenomas. Thus, the adequacy of bowel preparation should be accurately evaluated and documented. However, the accuracy of current bowel preparation quality scales greatly relies on intra-observer and inter-observer consistency for lack of objective measurements. Recently, deep learning based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. While, no studies have been conducted to evaluate the performance of deep learning algorithm in bowel preparation quality scoring. This study aims to train an algorithm to assess bowel preparation quality using the BBPS, and testify whether the engagement of AI can improve the quality control parameters of colonoscopy.

02

Conditions studied

  • Bowel Preparation

Keywords

  • Bowel Preparation, Deep Learning, Central Neural Networks
03

In context

Lead sponsor

Shandong University is the lead sponsor of 291 studies on the registry; 59 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 70 Years
Sexes eligible
All
Accepts healthy volunteers
No

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • Patients aged 18-70 years undergoing afternoon colonoscopy

Exclusion criteria

Exclusion Criteria:

  • Known or suspected bowel obstruction, stricture or perforation
  • Compromised swallowing reflex or mental status
  • Severe chronic renal failure(creatinine clearance \< 30 ml/min)
  • Severe congestive heart failure (New York Heart Association class III or IV)
  • Uncontrolled hypertension (systolic blood pressure > 170 mm Hg, diastolic blood pressure > 100 mm Hg)
  • Dehydration
  • Disturbance of electrolytes
  • Pregnancy or lactation
  • Hemodynamically unstable
  • Unable to give informed consent
05

Study design

Phase
Not applicable
Primary purpose
Health services research
Allocation
Randomized
Intervention model
Parallel assignment
Masking
Single (Outcomes assessor)
Enrollment
100 participants (estimated)

Study arms

  • Experimental
    Artificial Intelligence assisted Scoring Group

    Patients in this group go through colonoscopy under the AI monitoring device.

    Device: Artificial intelligence assisted bowel preparation quality scoring system

  • Active comparator
    Conventional Human Scoring Group

    Patients in this group go through conventional colonoscopy without AI monitoring device.

    Device: Conventional human scoring

Interventions

  • DeviceArtificial intelligence assisted bowel preparation quality scoring system

    After receiving standard bowel preparation regimen, patients go through colonoscopy under the AI monitoring device. During the withdrawal process, bowel preparation quality is monitored by AI-associated scoring system. Whenever a sub-score below 2 points is detected, endoscopist will be alarmed up to three times to wash and suck the colonic contents. Videos will be recorded and re-evaluated by experts to determine the final BBPS score. The withdrawal time is targeted at least 6min in accordance with colonoscopy quality practice. All detected polyps will be removed and obtained for histological assessment, with the possible exception of diminutive(less than 5mm) rectal polyps.

  • DeviceConventional human scoring

    After receiving standard bowel preparation regimen, patients go through conventional colonoscopy without the AI monitoring device. During the withdrawal process, after washing and sucking the colonic contents according to endoscopist's personal experience, bowel preparation quality is evaluated by human. Videos will be recorded and re-evaluated by experts to determine the final BBPS score. The withdrawal time is targeted at least 6min in accordance with colonoscopy quality practice. All detected polyps will be removed and obtained for histological assessment, with the possible exception of diminutive(less than 5mm) rectal polyps.

06

What researchers measure

Primary outcomes

  1. The rate of patients achieving adequate bowel preparation in each group.

    Bowel preparation quality was measured by BBPS. After fully washing or suctioning of colonic contents, three segments including right colon (containing cecum and ascending colon), transvers colon (containing hepatic and splenic flexures) and left colon (containing descending and sigmoid colon) were individually scored from 0 to 3. Point 0 refers to unprepared colon segment with obscured solid stool making mucosa cannot be seen; Point 1 refers to part of mucosa can be seen, but some areas are covered by staining, residual stool, and/or opaque liquid; Point 2 refers to entire mucosa is well-seen; Point 3 refers to clean colon segment without staining, fecal materials or liquids. A sub-score of each colon segment was used, ranging from minimum 0 to maximum 3. The highest score means the excellent bowel preparation. Adequate bowel preparation was defined as a total BBPS≥6 and sub-BBPS≥2 per segment.

    Time frame: 6 months

Secondary outcomes

  1. Adenoma Detection Rate

    The proportion of patients from whom at least one adenoma can be detected.

    Time frame: 6 months

  2. Polyp Detection Rate

    The proportion of patients from whom at least one polyp can be detected.

    Time frame: 6 months

07

Study locations

1 site
  • Qilu hosipital
    Jinan, Shandong 257000, China
08

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Apr 9, 2019, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
09

Registry details

Key details

Study ID
NCT03908645
Responsible party
Xiuli Zuo (director of Qilu Hospital gastroenterology department, Shandong University) — Principal investigator
First posted
Apr 9, 2019
Start date
Dec 15, 2018
Primary completion
Dec 15, 2019 (estimated)
Completion
Apr 15, 2020 (estimated)
Last update
Apr 9, 2019

Study contacts

Xiuli Zuo, MD,PhD
principal investigator · Qilu Hospital of Shandong University

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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