Artificial Intelligence and Bowel Cleansing Quality

NCT05871814 | Completed

• 1 other identifier: Bowel Cleansing application
• Study Type: interventional
• Target: 774
• Locations: 1 country
• Sites: 1

Brief Summary

The main purpose of the study is to assess if a strategy based on a mobile application linked to a neural network is useful for guiding colon cleansing in a more personalized way is better than the usual care defined as regular oral and written instructions. The secondary aim will be the acceptance of this artificial intelligence device defined as the proportion of patients assigned to the intervention group that actually used the device.

Conditions

Cleansing Quality of the Colon

Outcome Measures

Primary Outcomes (1)

• Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale

  The Boston Bowel Preparation Scale assesses the quality of bowel cleansing in the three segments of the colon (proximal, transverse, and distal) on a scale of 0 (no preparation) to 3 points (excellent preparation), with a maximum score of 9 points.

  3 months

Secondary Outcomes (1)

• Participation rate

  3 months

Study Arms (2)

Colon preparation guided by an artificial intelligence device

EXPERIMENTAL

Regular oral and written information will be provided to this group. In addition, participants will take a picture of the last rectal effluent with the smart phone that have to upload to a server. A convolutional neural network will assess whether the bowel preparation is correct or not (clean or not). The system will issue specific recommendations based on the quality of cleansing.

Device: Colon preparation guided by an artificial intelligence device

Control group

ACTIVE COMPARATOR

Regular oral and written information will be provided to this group

Device: Colon preparation guided by an artificial intelligence device

Interventions

Colon preparation guided by an artificial intelligence device DEVICE

Regular oral and written information will be provided to this group. In addition, participants will take a picture of the last rectal effluent with the smart phone that have to upload to a server. A convolutional neural network will assess whether the bowel preparation is correct or not (clean or not). The system will issue specific recommendations based on the quality of cleansing

Colon preparation guided by an artificial intelligence device; Control group

Eligibility Criteria

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

You may qualify if:

• Age ≥ 18 years.
• Patients referred for outpatient colonoscopy
• Sign informed consent

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.
• Inability to use the smartphone application

Sponsors & Collaborators

• University of La Laguna: lead

Study Sites (1)

Hospital Universitario de Canarias

San Cristóbal de La Laguna, Santa Cruz de Tenerife, 38320, Spain

Location

Related Publications (4)

• Gimeno-Garcia AZ, Benitez-Zafra F, Hernandez A, Hernandez-Negrin D, Nicolas-Perez D, Hernandez G, Baute-Dorta JL, Cedres Y, Del-Castillo R, Mon J, Jimenez A, Navarro-Davila MA, Rodriguez-Hernandez E, Alarcon O, Romero R, Felipe V, Segura N, Hernandez-Guerra M. Agreement between the perception of colon cleansing reported by patients and colon cleansing assessed by a validated colon cleansing scale. Gastroenterol Hepatol. 2024 Feb;47(2):130-139. doi: 10.1016/j.gastrohep.2023.02.009. Epub 2023 Mar 2. English, Spanish.

  PMID: 36870478 BACKGROUND

• 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 BACKGROUND

• Mori Y, East JE, Hassan C, Halvorsen N, Berzin TM, Byrne M, von Renteln D, Hewett DG, Repici A, Ramchandani M, Al Khatry M, Kudo SE, Wang P, Yu H, Saito Y, Misawa M, Parasa S, Matsubayashi CO, Ogata H, Tajiri H, Pausawasdi N, Dekker E, Ahmad OF, Sharma P, Rex DK. Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement. Dig Endosc. 2023 May;35(4):422-429. doi: 10.1111/den.14531. Epub 2023 Mar 13.

  PMID: 36749036 BACKGROUND

• Gimeno-Garcia AZ, Benitez-Zafra F, Redondo-Zaera I, Cruz-Perdomo N, Bautista M, Morales-Arraez D, Pardo-Balteiro A, Borque P, Navarro-Davila MA, Jimenez-Sosa A, Berenguer R, Tellechea J, Alayon-Miranda S, Mon J, Romero A, Alvarez L, Del Castillo R, Perdomo A, Hernandez-Negrin D, Gamez S, Cedres Y, Quintana-Diaz PH, Perez-Gonzalez F, Nicolas-Perez D, Hernandez-Guerra M. An Artificial Intelligence-Guided Strategy to Reduce Poor Bowel Preparation: A Multicenter Randomized Controlled Study. Am J Gastroenterol. 2026 Jan 20. doi: 10.14309/ajg.0000000000003921. Online ahead of print.

  PMID: 41556527 DERIVED

Study Officials

• Antonio Z Gimeno García, MD, PhD

  Hospital Universitario de Canarias

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: INVESTIGATOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Medical Doctor

Study Record Dates

• First Submitted: May 13, 2023
• First Posted: May 23, 2023
• Study Start: September 15, 2023
• Primary Completion: August 30, 2024
• Study Completion: August 31, 2024
• Last Updated: June 4, 2025

Record last verified: 2025-06

Locations

ES (1)