Bowel PreparationNCT03908645
Development and Validation of a Deep Learning Algorithm for Bowel Preparation Quality Scoring
A study testing Artificial intelligence assisted bowel preparation quality scoring system and Conventional human scoring in people with Bowel Preparation, run by Shandong University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Bowel Preparation
- What is being tested?
- Artificial intelligence assisted bowel preparation quality scoring system and Conventional human scoring
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 15 Dec 2018to 15 Apr 2020
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients aged 18-70 years undergoing afternoon colonoscopy
Who cannot join
- 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
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- Disturbance of electrolytes
- Pregnancy or lactation
- Hemodynamically unstable
- Unable to give informed consent
Trial locations and contacts
1 site in 1 country.
About this study
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.
- Conditions
- Bowel Preparation
- Being tested
- Artificial intelligence assisted bowel preparation quality scoring system, Conventional human scoring
- Keywords
- Bowel Preparation, Deep Learning, Central Neural Networks
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence assisted bowel preparation quality scoring systemDevice
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.
Conventional human scoringDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence assisted Scoring Group | Experimental | Patients in this group go through colonoscopy under the AI monitoring device. Receives: Device: Artificial intelligence assisted bowel preparation quality scoring system |
| Conventional Human Scoring Group | Active comparator | Patients in this group go through conventional colonoscopy without AI monitoring device. Receives: Device: Conventional human scoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 6 months
Secondary outcomes (2)
Adenoma Detection Rate
The proportion of patients from whom at least one adenoma can be detected.
Measured: 6 months
Polyp Detection Rate
The proportion of patients from whom at least one polyp can be detected.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Apr 2019
- Study start15 Dec 2018
- Primary completion15 Dec 2019 (estimated)
- Study completion15 Apr 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bowel PreparationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiuli Zuo, Shandong University
- Investigators
- Xiuli Zuo, MD,PhD, Principal investigator, Qilu Hospital of Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03908645
- Sponsor's protocol ID
- 2019SDU-QILU-G001
- Status last verified
- Apr 2019
- First submitted
- 8 Apr 2019
- Last updated
- 9 Apr 2019
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Apr 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning Algorithm for Bowel Preparation Quality Scoring
- ClinicalTrials.gov ID
- NCT03908645
- Acronym / sponsor ID
- 2019SDU-QILU-G001
- Conditions
- Bowel Preparation
- Keywords
- Bowel Preparation, Deep Learning, Central Neural Networks
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 15 Dec 2018 (actual)
- Primary completion
- 15 Dec 2019 (estimated)
- Study completion
- 15 Apr 2020 (estimated)
- First posted
- 9 Apr 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence assisted Scoring Group | Experimental | Device: Artificial intelligence assisted bowel preparation quality scoring systemPatients in this group go through colonoscopy under the AI monitoring device. |
| Conventional Human Scoring Group | Active comparator | Device: Conventional human scoringPatients in this group go through conventional colonoscopy without AI monitoring device. |
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Patients aged 18-70 years undergoing afternoon colonoscopy
Key exclusion criteria (10 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence assisted bowel preparation quality scoring systemAfter 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. | Device | Artificial Intelligence assisted Scoring Group |
| Conventional human scoringAfter 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. | Device | Conventional Human Scoring Group |
Primary outcome
The rate of patients achieving adequate bowel preparation in each group.
Time frame: 6 months
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.
2 secondary outcomes
Adenoma Detection Rate
Time frame: 6 months
Polyp Detection Rate
Time frame: 6 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Apr 2019.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Xiuli Zuo, MD,PhD principal investigator
Qilu Hospital of Shandong University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence assisted bowel preparation quality scoring system and Conventional human scoring in people with Bowel Preparation, run by Shandong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 9 Apr 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The rate of patients achieving adequate bowel preparation in each group. (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Apr 2019.