NCT05871814|Completed
Artificial Intelligence and Bowel Cleansing Quality
CALPER3
Strategy for Decreasing Inadequate Bowel Cleansing in Colonoscopy Based on an Artificial Intelligence System: A Randomized and Controlled Study
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CALPER3
Strategy for Decreasing Inadequate Bowel Cleansing in Colonoscopy Based on an Artificial Intelligence System: A Randomized and Controlled Study
Sponsor study identifier
interventional
774
1 country
1
Study Start
September 15, 2023
Actual (reported)Primary Completion
August 30, 2024
Actual (reported)Study Completion
August 31, 2024
Actual (reported)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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
August 31, 2024
Actual (reported)Click on a node to explore related trials.
Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
May 13, 2023
Actual (reported)May 23, 2023
Actual (reported)September 15, 2023
Actual (reported)August 30, 2024
Actual (reported)August 31, 2024
Actual (reported)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
Participation rate
3 months
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.
Regular oral and written information will be provided to this group
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
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
Hospital Universitario de Canarias
San Cristóbal de La Laguna, Santa Cruz de Tenerife, 38320, Spain
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.
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.
Antonio Z Gimeno García, MD, PhD
Hospital Universitario de Canarias
774 participants (actual enrollment reported by the registry).
First Submitted
May 13, 2023
First Posted
May 23, 2023
Study Start
September 15, 2023
Actual (reported)Impact of a Predictive Score of Bowel Preparation Quality in Clinical Practice
Contact the study team to confirm eligibility.
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.
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, Castillo RD, 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 Apr 1;121(4):890-898. doi: 10.14309/ajg.0000000000003921. Epub 2026 Jan 20.
Primary Completion
August 30, 2024
Actual (reported)Study Completion
August 31, 2024
Actual (reported)Last Updated
June 4, 2025
Record last verified: 2025-06
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Source: ClinicalTrials.gov.
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