Artificial Intelligence and Bowel Cleansing Quality

Cleansing Quality of the Colon Clinical Trial

— CALPER3  

Official title:

Strategy for Decreasing Inadequate Bowel Cleansing in Colonoscopy Based on an Artificial Intelligence System: A Randomized and Controlled Study

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05871814
• Other study ID #: Bowel Cleansing application
• Status: Not yet recruiting
• Phase: N/A
• Start date: November 30, 2023
• Est. completion date: October 15, 2024

Study information

• Verified date: November 2023
• Source: University of La Laguna
• Contact: Antonio Z Gimeno Garcia, MD, PhD
• Phone: 34-922678039
• Email: antozeben[@]gmail.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 774
• Est. completion date: October 15, 2024
• Est. primary completion date: September 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Cleansing Quality of the Colon

Intervention

Device:
• 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

Locations

Country	Name	City	State
Spain	Hospital Universitario de Canarias	La Laguna	Santa Cruz De Tenerife

Sponsors (1)

Lead Sponsor	Collaborator
University of La Laguna

Country where clinical trial is conducted

Spain, 

References & Publications (3)

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 — View Citation

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-Guer — View Citation

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, — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Participation rate	Proportion of participants assigned to the intervention group who used the device. It will be assessed by self-reported information from the patients and by the presence of a picture in a server for the storage of images.	3 months