Artificial Intelligence and Bowel Cleansing Quality

NCT05553977 | Unknown

• 1 other identifier: CNN bowel cleansing
• Study Type: observational
• Target: 667
• Locations: 1 country
• Sites: 1

Brief 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 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).

Conditions

Cleansing Quality of the Colon

Keywords

Bowel cleansing; Colonoscopy; Convolutional Neural Network

Outcome Measures

Primary Outcomes (2)

• 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.

  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

  1 years

Study Arms (1)

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

Drug: Bowel preparation for colonoscopy; Procedure: Colonoscopy

Interventions

Bowel preparation for colonoscopy DRUG

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.

Consecutive patients for outpatient colonoscopy

Colonoscopy PROCEDURE

Colonoscopy will be performed to every patient included in the study

Consecutive patients for outpatient colonoscopy

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• Age \>18, to sign the informed consent,
• Patients with indication of outpatient colonoscopy
• Patients ingesting the bowel preparation

You may not qualify if:

• 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

Sponsors & Collaborators

• Hospital Universitario de Canarias: lead

Study Sites (1)

Department of Gastroenterology

San Cristóbal de La Laguna, S/C de Tenerife, 38320, Spain

RECRUITING

Related Publications (5)

• 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.

  PMID: 35318734 RESULT

• Berzin 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.

  PMID: 32565188 RESULT

• Fatima 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.

  PMID: 20362286 RESULT

• Alzubaidi 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.

  PMID: 33816053 RESULT

• Harewood 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.

  PMID: 15128347 RESULT

MeSH Terms

Interventions

Colonoscopy

Intervention Hierarchy (Ancestors)

Endoscopy, Gastrointestinal; Endoscopy, Digestive System; Diagnostic Techniques, Digestive System; Diagnostic Techniques and Procedures; Diagnosis; Endoscopy; Diagnostic Techniques, Surgical; Digestive System Surgical Procedures; Surgical Procedures, Operative; Minimally Invasive Surgical Procedures

Central Study Contacts

Antonio Z Gimeno García, MD, PhD

CONTACT

+34922678554; antozeben@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 19, 2022
• First Posted: September 26, 2022
• Study Start: October 1, 2022
• Primary Completion: April 20, 2023
• Study Completion: May 30, 2023
• Last Updated: January 18, 2023

Record last verified: 2023-01

Locations

ES (1)