Artificial Intelligence and Bowel Cleansing Quality

NCT ID: NCT05871814

Last Updated: 2025-06-04

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 774 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-09-15
• Study Completion Date: 2024-08-31

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.

Detailed Description

The patient's perception of colon cleanliness prior to undergoing a colonoscopy has been studied as a predictor of colon cleanliness quality, demonstrating to be a powerful predictor of inadequate cleanliness. A convolutional neural network developed by our group, trained with photographs of rectal effluents at different moments of colon preparation, has achieved high diagnostic accuracy. Based on all this experience, the next step would be to evaluate in a randomized clinical trial whether this neural network integrated into a computer application associated with cleaning recommendations improves the colon cleanliness quality of patients compared to a control group, being the objective of this project Therefore, 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. Consecutive outpatient patients meeting inclusion criteria and none of the exclusion criteria who have been requested to undergo colonoscopy will be included in the study and randomized to mobile artificial intelligence application or control group The intervention group will receive a response from the AI system in order to determine the quality of colon cleansing: adequate preparation or inadequate preparation. In addition, the system will issue specific recommendations based on the quality of cleansing. Patients assigned to the control group will undergo colonoscopy preparation according to standard recommendations.

Conditions

Cleansing Quality of the Colon

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: SINGLE

Study Groups

Colon preparation guided by an artificial intelligence 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.

Group Type: EXPERIMENTAL

Colon preparation guided by an artificial intelligence device

Intervention Type: 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

Control group

Regular oral and written information will be provided to this group

Group Type: ACTIVE_COMPARATOR

Colon preparation guided by an artificial intelligence device

Intervention Type: 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

Interventions

Colon preparation guided by an artificial intelligence 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

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of La Laguna

OTHER

Sponsor Role: lead

Responsible Party

Manuel Hernandez-Guerra, MD

Medical Doctor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Antonio Z Gimeno García, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Hospital Universitario de Canarias

Locations

Hospital Universitario de Canarias

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

Countries

Spain

References

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.

Reference Type: BACKGROUND

PMID: 36870478 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 32565188 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 36749036 (View on PubMed)

Other Identifiers

Bowel Cleansing application

Identifier Type: -

Identifier Source: org_study_id