Improving Polyp Detection Rate by Artificial Intelligence in Colonoscopy

Study Overview
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Brief Summary
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The aim of this study is to investigate if the use of artificial intelligence (AI) in colonoscopy improves the polyp detection rate, and if the use of AI has a learning effect.

Detailed Description
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The endoscopists will use GI Genius from Medtronic, a device that uses artificial intelligence (AI) based on machine learning to detect polyps in the colon in real time during colonoscopy. The device interprets the endoscopy pictures and superimposes possible polyps with frames. The patients will be included in regular outpatient clinics in Western Norway. The endoscopists will be divided into groups depending on their experience. The endoscopists will perform colonoscopies in three phases; (1) before the use of AI, (2) during the use of AI and (3) after the use of AI. The investigators will then evaluate the polyp detection rate (PDR) in the three phases to see if AI increases PDR, and if there is a learning effect on PDR after the use of AI. The investigators will also evaluate if there is a difference in the learning-effect from AI-use depending on if the endoscopist is experienced or inexperienced. The PDR's are registered as part of Norway's national quality register of colonoscopy, Gastronet. The data registered in Gastronet can also help the investigators evaluate other outcomes such as withdrawal time, bowel preparation, patient reported pain, patient satisfaction and complications.

Official Title
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Improving Polyp Detection Rate by Artificial Intelligence in Colonoscopy

Conditions
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Colonic Polyp

Intervention / Treatment
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* Device: GI Genius
* Device: Standard white light colonoscope


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients coming to outpatient clinics to perform colonoscopies Exclusion Criteria: Total colectomy Reservation against registration in Gastronet, the national quality register for colonoscopy in Norway
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Non-Randomized
Intervention Model: Crossover Assignment
Interventional Model Description: Endoscopists will perform colonoscopies in three consecutive phases; 1. without artificial intelligence (AI), 2. with AI, 3. without (AI).
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: With artificial intelligence (AI)<br>Use of GI Genius artificial intelligence device during colonoscopy. | Device: GI Genius<br>* The use of artificial intelligence during colonoscopy to improve polyp detection.<br>|
| Active Comparator: Without artificial intelligence<br>Use of standard colonoscopy equipment without GI Genius. | Device: Standard white light colonoscope<br>* Standard colonoscopy.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Polyp detection rate (PDR) with and without artificial intelligence (AI) | Evaluate the PDR with and without the use of GI Genus artificial intelligence | 18 months | 
| PDR after the use of AI, is there a learning effect? | Evaluate if there is an improved PDR after the use of AI | 18 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Withdrawal time | To evaluate if the withdrawal time is influenced by the use of artificial intelligence | 18 months | 
| Complications | To evaluate if there are more registered complications with the use of AI | 24 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Colonoscopy, Artificial intelligence, Polyp detection