Artificial Intelligence and Bowel Cleansing Quality

Study Overview
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Brief Summary
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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).

Detailed Description
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The patient perception of the last bowel movement before the colonoscopy has been shown a powerful predictor of bowel cleansing rated during colonoscopy. A large study involving 1011 patients distributed in a derivation cohort (633 patients) and a validation cohort (378 patients) using a set of 4 pictures resembling bowel cleansing qualities showed a moderate agreement with the BBPS. In addition, a good agreement was found when the staff perception and patient perception of the last bowel movement were compared. These findings offer an excellent opportunity to test rescue cleansing interventions the same day of the examination, before colonoscopy. Over the last two years, artificial intelligence applications have wrought a substantial breakthrough in several disciplines, including endoscopy. Machine learning and its more advanced form deep learning, refers to the development of algorithms (convolutional neural networks) with the ability to learn and perform certain tasks. In the endoscopy setting, computer vision applications have been stated as research priority field. Based on all this experience, the aim of this study was to design and to validate a convolutional neural network capable of automatically predicting the quality of the patient cleansing at home after the intake of the bowel cleansing solution and before attending the colonoscopy. The other aim was to prospectively assess in a cohort of patients the agreement between the result of the last rectal effluent quality assessed by the convolutional neural network and the cleansing quality assessed during the colonoscopy assessed by a validated scale (Boston Bowel Preparation Scale, BBPS) This study is nested in an observational prospective study conducted at the Open Access Endoscopy Unit of the Hospital Universitario de Canarias between February 2021 and May 2021 (NCT04702646). A total of 633 consecutive outpatients with a scheduled colonoscopy participated in this study (a total of 266 patients (42%) sent at least one picture). After this study, patients in whom an outpatient colonoscopy was requested, were asked to provide pictures of their effluents during bowel preparation intake. A subgroup of these images will be classified by the personal of our unit in adequate and inadequate and will be used to train the convolutional neural network. Another set of images will be used to validate the convolutional neural network. Additionally, the investigators will validate in-vivo the convolutional neural network comparing its classification of the effluent quality with a validated colon cleansing scale during the colonoscopy.

Official Title
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Design and Validation of an Artificial Intelligence System to Detect the Quality of Colon Cleansing Before Colonoscopy

Conditions
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Cleansing Quality of the Colon

Intervention / Treatment
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* Drug: Bowel preparation for colonoscopy
* Procedure: Colonoscopy


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age >18, to sign the informed consent, Patients with indication of outpatient colonoscopy Patients ingesting the bowel preparation 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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Consecutive patients for outpatient colonoscopy<br>The researchers will offer to participate in the study to patients scheduled for a colonoscopy who meet all the inclusion criteria and none of the exclusion criteria | Drug: Bowel preparation for colonoscopy<br>* 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.<br>Procedure: Colonoscopy<br>* Colonoscopy will be performed to every patient included in the study<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Effluent characteristics | Effluent characteristics. Set of 4 pictures categorized in adequate preparation (clear liquid, clear liquid with lumps) and inadequate preparation (dark liquid, or dark liquid with solid particles). The concolutional Neural Network will be trained with effluent images and validated. | 1 year | 
| Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale | Quality of bowel cleansing assessed by the Boston Bowel Preparation Scale. This scale goes from 0 (no preparation) to 3 points (excellent preparation) in the three segments of the colon (proximal, transverse and distal). The maximum score is 9 points | 1 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Bowel cleansing, Colonoscopy, Convolutional Neural Network