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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Colon preparation guided by an artificial intelligence device | Experimental | 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. |
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| Control group | Active Comparator | Regular oral and written information will be provided to this group |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Colon preparation guided by an artificial intelligence device | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Antonio Z Gimeno GarcÃa, MD, PhD | Hospital Universitario de Canarias | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Universitario de Canarias | San Cristóbal de La Laguna | Santa Cruz de Tenerife | 38320 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36870478 | Result | 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. | |
| 32565188 |
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| Result |
| 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. |
| 36749036 | Result | 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. |
| 41556527 | Derived | 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. |