Cleansing Quality of the ColonCALPER2NCT05553977
Artificial Intelligence and Bowel Cleansing Quality
An observational study of Cleansing Quality of the Colon, run by Hospital Universitario de Canarias at 1 site in Spain.
Trial summary
The main purpose of the study is to design and validate a convolutional neural network (CNN) with the ability to discriminate between pictures of effluents with different qualities of bowel cleansing and in a second time to prospectively assess in a cohort of patients the agreement between the result of the last… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cleansing Quality of the Colon
- What is being tested?
- Bowel preparation for colonoscopy and Colonoscopy
- Type of treatment
- Surgery or procedure
- How many people
- 667 participants
- Where
- 1 siteSpain
- Study dates
- 1 Oct 2022to 30 May 2023
- Phase
- Observational
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The researchers will offer to participate in the study to patients scheduled for a colonoscopy who meet all the inclusion criteria and none of the exclusion criteria. The researchers will explain the purpose of the study and will ask to sign the informed consent. They will give verbal and written information.
Who may be able to join
- Age >18, to sign the informed consent,
- Patients with indication of outpatient colonoscopy
- Patients ingesting the bowel preparation
Who cannot join
- Incomplete colonoscopy (except for poor bowel preparation)
- Contraindication for colonoscopy
- Allergies.
- Refusal to participate in the study or impairment to sign the informed consent.
- Colectomy (more than 1 segment)
- Dementia with difficulty in the intake of the preparation
Trial locations and contacts
1 site in 1 country.
Spain1 site, 1 recruiting
- Recruiting
Department of Gastroenterology
San Cristóbal de La Laguna, S/C de Tenerife, 38320
Antonio Z Gimeno Garcia, MD, hD, 34922678039, antozeben@gmail.com
David Nicolas Perez, MD, PhD
About this study
Official title: Design and Validation of an Artificial Intelligence System to Detect the Quality of Colon Cleansing Before Colonoscopy
The main purpose of the study is to design and validate a convolutional neural network (CNN) with the ability to discriminate between pictures of effluents with different qualities of bowel cleansing and in a second time to prospectively assess in a cohort of patients the agreement between the result of the last rectal effluent quality assessed by the CNN and the cleansing quality assessed during the colonoscopy assessed by a validated scale (Boston Bowel Preparation Scale, BBPS). Patients will be prepared with polyethylene glycol (PEG), PEG plus ascorbic acid (PEG-Asc) or sodium picosulfate-oxide magnesium solution (PS).
Read the detailed description
The patient perception of the last bowel movement before the colonoscopy has been shown a powerful predictor of bowel cleansing rated during colonoscopy. A large study involving 1011 patients distributed in a derivation cohort (633 patients) and a validation cohort (378 patients) using a set of 4 pictures resembling bowel cleansing qualities showed a moderate agreement with the BBPS. In addition, a good agreement was found when the staff perception and patient perception of the last bowel movement were compared. These findings offer an excellent opportunity to test rescue cleansing interventions the same day of the examination, before colonoscopy.
Over the last two years, artificial intelligence applications have wrought a substantial breakthrough in several disciplines, including endoscopy. Machine learning and its more advanced form deep learning, refers to the development of algorithms (convolutional neural networks) with the ability to learn and perform certain tasks. In the endoscopy setting, computer vision applications have been stated as research priority field. Based on all this experience, the aim of this study was to design and to validate a convolutional neural network capable of automatically predicting the quality of the patient cleansing at home after the intake of the bowel cleansing solution and before attending the colonoscopy. The other aim was to prospectively assess in a cohort of patients the agreement between the result of the last rectal effluent quality assessed by the convolutional neural network and the cleansing quality assessed during the colonoscopy assessed by a validated scale (Boston Bowel Preparation Scale, BBPS) This study is nested in an observational prospective study conducted at the Open Access Endoscopy Unit of the Hospital Universitario de Canarias between February 2021 and May 2021 (NCT04702646). A total of 633 consecutive outpatients with a scheduled colonoscopy participated in this study (a total of 266 patients (42%) sent at least one picture). After this study, patients in whom an outpatient colonoscopy was requested, were asked to provide pictures of their effluents during bowel preparation intake. A subgroup of these images will be classified by the personal of our unit in adequate and inadequate and will be used to train the convolutional neural network. Another set of images will be used to validate the convolutional neural network. Additionally, the investigators will validate in-vivo the convolutional neural network comparing its classification of the effluent quality with a validated colon cleansing scale during the colonoscopy.
- Conditions
- Cleansing Quality of the Colon
- Being tested
- Bowel preparation for colonoscopy, Colonoscopy
- Keywords
- Bowel cleansing, Colonoscopy, Convolutional Neural Network
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Bowel preparation for colonoscopyDrug
one day liquid diet will be administered to every patient included in the study and: split-dose bowel preparation with 4 Liters of Polyethylene glycol solution, 2 Liters of PEG-Ascorbic acid or 2 Liters Picosulfate.
ColonoscopyProcedure or surgery
Colonoscopy will be performed to every patient included in the study
| Study group | Role | What they receive |
|---|---|---|
| Consecutive patients for outpatient colonoscopy | The researchers will offer to participate in the study to patients scheduled for a colonoscopy who meet all the inclusion criteria and none of the exclusion criteria Receives: Drug: Bowel preparation for colonoscopy; Procedure: Colonoscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Effluent characteristics
Effluent characteristics. Set of 4 pictures categorized in adequate preparation (clear liquid, clear liquid with lumps) and inadequate preparation (dark liquid, or dark liquid with solid particles). The concolutional Neural Network will be trained with effluent images and validated.
Measured: 1 year
Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale
Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale. This scale goes from 0 (no preparation) to 3 points (excellent preparation) in the three segments of the colon (proximal, transverse and distal). The maximum score is 9 points
Measured: 1 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Sep 2022
- Study start1 Oct 2022
- Primary completion20 Apr 2023 (estimated)
- Study completion30 May 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Cleansing Quality of the ColonStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 667 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospital Universitario de Canarias
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05553977
- Sponsor's protocol ID
- CNN bowel cleansing
- Acronym
- CALPER2
- Status last verified
- Jan 2023
- First submitted
- 19 Sep 2022
- Last updated
- 18 Jan 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Mori Y, Misawa M, Kudo SE. Challenges in artificial intelligence for polyp detection. Dig Endosc. 2022 May;34(4):870-871. doi: 10.1111/den.14279. Epub 2022 Mar 22. No abstract available.
PubMed 35318734Berzin TM, Parasa S, Wallace MB, Gross SA, Repici A, Sharma P. Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Gastrointest Endosc. 2020 Oct;92(4):951-959. doi: 10.1016/j.gie.2020.06.035. Epub 2020 Jun 19.
PubMed 32565188Fatima H, Johnson CS, Rex DK. Patients' description of rectal effluent and quality of bowel preparation at colonoscopy. Gastrointest Endosc. 2010 Jun;71(7):1244-1252.e2. doi: 10.1016/j.gie.2009.11.053. Epub 2010 Apr 1.
PubMed 20362286Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaria J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1):53. doi: 10.1186/s40537-021-00444-8. Epub 2021 Mar 31.
PubMed 33816053Harewood GC, Wright CA, Baron TH. Assessment of patients' perceptions of bowel preparation quality at colonoscopy. Am J Gastroenterol. 2004 May;99(5):839-43. doi: 10.1111/j.1572-0241.2004.04176.x.
PubMed 15128347
Study at a glance
From the registry record, last updated by the sponsor on 18 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Design and Validation of an Artificial Intelligence System to Detect the Quality of Colon Cleansing Before Colonoscopy
- ClinicalTrials.gov ID
- NCT05553977
- Acronym / sponsor ID
- CALPER2 / CNN bowel cleansing
- Conditions
- Cleansing Quality of the Colon
- Keywords
- Bowel cleansing, Colonoscopy, Convolutional Neural Network
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 667 participants (estimated)
- Lead sponsor
- Hospital Universitario de CanariasUniversity, hospital or other
- Study start
- 1 Oct 2022 (actual)
- Primary completion
- 20 Apr 2023 (estimated)
- Study completion
- 30 May 2023 (estimated)
- First posted
- 26 Sep 2022 (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 main purpose of the study is to design and validate a convolutional neural network (CNN) with the ability to discriminate between pictures of effluents with different qualities of bowel cleansing and in a second time to prospectively assess in a cohort of patients the agreement between the result of the last rectal effluent quality assessed by the CNN and the cleansing quality assessed during the colonoscopy assessed by a validated scale (Boston Bowel Preparation Scale, BBPS). Patients will be prepared with polyethylene glycol (PEG), PEG plus ascorbic acid (PEG-Asc) or sodium picosulfate-oxide magnesium solution (PS).
Detailed description
The patient perception of the last bowel movement before the colonoscopy has been shown a powerful predictor of bowel cleansing rated during colonoscopy. A large study involving 1011 patients distributed in a derivation cohort (633 patients) and a validation cohort (378 patients) using a set of 4 pictures resembling bowel cleansing qualities showed a moderate agreement with the BBPS. In addition, a good agreement was found when the staff perception and patient perception of the last bowel movement were compared. These findings offer an excellent opportunity to test rescue cleansing interventions the same day of the examination, before colonoscopy.
Over the last two years, artificial intelligence applications have wrought a substantial breakthrough in several disciplines, including endoscopy. Machine learning and its more advanced form deep learning, refers to the development of algorithms (convolutional neural networks) with the ability to learn and perform certain tasks. In the endoscopy setting, computer vision applications have been stated as research priority field. Based on all this experience, the aim of this study was to design and to validate a convolutional neural network capable of automatically predicting the quality of the patient cleansing at home after the intake of the bowel cleansing solution and before attending the colonoscopy. The other aim was to prospectively assess in a cohort of patients the agreement between the result of the last rectal effluent quality assessed by the convolutional neural network and the cleansing quality assessed during the colonoscopy assessed by a validated scale (Boston Bowel Preparation Scale, BBPS) This study is nested in an observational prospective study conducted at the Open Access Endoscopy Unit of the Hospital Universitario de Canarias between February 2021 and May 2021 (NCT04702646). A total of 633 consecutive outpatients with a scheduled colonoscopy participated in this study (a total of 266 patients (42%) sent at least one picture). After this study, patients in whom an outpatient colonoscopy was requested, were asked to provide pictures of their effluents during bowel preparation intake. A subgroup of these images will be classified by the personal of our unit in adequate and inadequate and will be used to train the convolutional neural network. Another set of images will be used to validate the convolutional neural network. Additionally, the investigators will validate in-vivo the convolutional neural network comparing its classification of the effluent quality with a validated colon cleansing scale during the colonoscopy.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Consecutive patients for outpatient colonoscopy | – | Drug: Bowel preparation for colonoscopy, Procedure: ColonoscopyThe researchers will offer to participate in the study to patients scheduled for a colonoscopy who meet all the inclusion criteria and none of the exclusion criteria |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The researchers will offer to participate in the study to patients scheduled for a colonoscopy who meet all the inclusion criteria and none of the exclusion criteria. The researchers will explain the purpose of the study and will ask to sign the informed consent. They will give verbal and written information.
Key inclusion criteria (3 listed)
- Age >18, to sign the informed consent,
- Patients with indication of outpatient colonoscopy
- Patients ingesting the bowel preparation
Key exclusion criteria (6 listed)
- Incomplete colonoscopy (except for poor bowel preparation)
- Contraindication for colonoscopy
- Allergies.
- Refusal to participate in the study or impairment to sign the informed consent.
- Colectomy (more than 1 segment)
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 |
|---|---|---|
| Bowel preparation for colonoscopyone day liquid diet will be administered to every patient included in the study and: split-dose bowel preparation with 4 Liters of Polyethylene glycol solution, 2 Liters of PEG-Ascorbic acid or 2 Liters Picosulfate. | Drug | Consecutive patients for outpatient colonoscopy |
| ColonoscopyColonoscopy will be performed to every patient included in the study | Procedure or surgery | Consecutive patients for outpatient colonoscopy |
Primary outcome
Effluent characteristics
Time frame: 1 year
Effluent characteristics. Set of 4 pictures categorized in adequate preparation (clear liquid, clear liquid with lumps) and inadequate preparation (dark liquid, or dark liquid with solid particles). The concolutional Neural Network will be trained with effluent images and validated.
Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale
Time frame: 1 years
Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale. This scale goes from 0 (no preparation) to 3 points (excellent preparation) in the three segments of the colon (proximal, transverse and distal). The maximum score is 9 points
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Jan 2023.
- Results publication: PubMed 35318734Mori Y, Misawa M, Kudo SE. Challenges in artificial intelligence for polyp detection. Dig Endosc. 2022 May;34(4):870-871. doi: 10.1111/den.14279. Epub 2022 Mar 22. No abstract available.
- Results publication: PubMed 32565188Berzin TM, Parasa S, Wallace MB, Gross SA, Repici A, Sharma P. Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Gastrointest Endosc. 2020 Oct;92(4):951-959. doi: 10.1016/j.gie.2020.06.035. Epub 2020 Jun 19.
- Results publication: PubMed 20362286Fatima H, Johnson CS, Rex DK. Patients' description of rectal effluent and quality of bowel preparation at colonoscopy. Gastrointest Endosc. 2010 Jun;71(7):1244-1252.e2. doi: 10.1016/j.gie.2009.11.053. Epub 2010 Apr 1.
- Results publication: PubMed 33816053Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaria J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1):53. doi: 10.1186/s40537-021-00444-8. Epub 2021 Mar 31.
- Results publication: PubMed 15128347Harewood GC, Wright CA, Baron TH. Assessment of patients' perceptions of bowel preparation quality at colonoscopy. Am J Gastroenterol. 2004 May;99(5):839-43. doi: 10.1111/j.1572-0241.2004.04176.x.
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cleansing Quality of the Colon, run by Hospital Universitario de Canarias at 1 site in Spain.
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 3 inclusion and 6 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 18 Jan 2023. 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 Spain. 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?
Contact Antonio Z Gimeno García, MD, PhD (+34922678554, antozeben@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Effluent characteristics (time frame: 1 year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05553977, which also lists 5 linked publications.
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 18 Jan 2023; AzTrials last refreshed its copy on 27 Oct 2026.