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Artificial Intelligence and Bowel Cleansing Quality

NCT05553977 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 667

Last updated 2023-01-18

No results posted yet for this study

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

Interventions

DRUG

Bowel preparation for colonoscopy

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.

PROCEDURE

Colonoscopy

Colonoscopy will be performed to every patient included in the study

Sponsors & Collaborators

  • Hospital Universitario de Canarias

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-10-01
Primary Completion
2023-04-20
Completion
2023-05-30

Countries

  • Spain

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05553977 on ClinicalTrials.gov