Artificial Intelligence and Bowel Cleansing Quality (CALPER3)

University of La Laguna

Status: Completed

Conditions: Cleansing Quality of the Colon

Treatments: Device: Colon preparation guided by an artificial intelligence device

Study type: Interventional

Funder types: Other

Identifiers

NCT05871814

Bowel Cleansing application

Details and patient eligibility

About

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.

Full 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.

Enrollment: 774 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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774 participants in 2 patient groups

Colon preparation guided by an artificial intelligence device

Experimental group

Description:

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.

Treatment:

Device: Colon preparation guided by an artificial intelligence device

Control group

Active Comparator group

Description:

Regular oral and written information will be provided to this group

Treatment:

Device: Colon preparation guided by an artificial intelligence device