Computer-aided Detection During Screening Colonoscopy

Study Overview
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Brief Summary
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Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Based on this, new technological tools aimed to improve the quality of the procedures, diminishing the technical and operator-related factors associated with the missed lesions. These tools use artificial intelligence (AI), a computer system able to perform human tasks after a previous training process from a large dataset. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan) is a newly developed detection system based on AI. It was designed to alert and direct the attention to potential mucosal lesions. According to its remarkable features, it may increase the polyp and adenoma detection rates (PDR and ADR, respectively) and decrease the adenoma miss rate (AMR). Based on the above, the investigators aim to assess the real-world effectiveness of the DiscoveryTM AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.

Detailed Description
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Colorectal cancer (CRC) is worldwide the second and third cancer-related cause of death in men and women, respectively. For the detection of lesions in the mucosa (premalignant and malignant), colonoscopy has been considered the gold standard. However, up to 25% of lesions can be missed during conventional colonoscopy. Some technical (i.e., bowel preparation) and operator-related (i.e., expertise, and fatigue) factors are related to these missing lesions. During the rapid-growing technological era, new tools were launched to improve the quality and performance of colonoscopies. Through the assistance of artificial intelligence (AI) an identification of a pattern can be achieved after a previous training from a large dataset of images. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan), is a computer-assisted polyp/adenoma detection system based on AI. It detects classic adenomas and flat lesions, distinguished features like mucus cap or rim of debris with the advantage of a real-time and simultaneous multiple polyp detection. It was developed to minimize the missed lesions increasing as a result the polyp detection rate (PDR) and the adenoma detection rate (ADR). Lately, published data evaluating the AI-assisted polyp detectors has demonstrate high sensitivity, specificity, and interobserver agreement. Due to the importance of CRC diagnosis and prompt treatment, and taking advantage of the newly introduced DiscoveryTM AI system, the investigators aim to assess the real-world effectiveness of this AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.

Official Title
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Real-time Computer-aided Polyp/Adenoma Detection During Screening Colonoscopy: a Single-center Crossover Trial

Conditions
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Colorectal Polyp, Colorectal Cancer, Colorectal Adenoma

Intervention / Treatment
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* Diagnostic Test: HD- colonoscopy
* Diagnostic Test: HD-colonoscopy assisted by AI


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Adults ≥45 years old Patients referred for screening colonoscopy Adequate bowel preparation, Boston Bowel Preparation Scale (BBPS) ≥8 Patients who authorized for endoscopic approach. Exclusion Criteria: Pregnancy Any clinical condition which makes endoscopy inviable. Patients with history of Colorectal Carcinoma. Patients with history of Inflammatory Bowel Disease (IBD) Inability to provide informed consent

Ages Eligible for Study
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Minimum Age: 45 Years
Maximum Age: 89 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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Design Details

Primary Purpose: Diagnostic
Allocation: Non-Randomized
Intervention Model: Crossover Assignment
Interventional Model Description: Blinded, single center, controlled, prospective trial
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: HD-colonoscopy + AI-HD colonoscopy<br>This group is comprised by patients >45 years of age submitted for diagnostic colonoscopy. In the same session a HD-colonoscopy will be performed followed by an HD-colonoscopy with artificial intelligence assistance. The second procedure will be performed by an operator with the same-level-of -expertise in comparison to the initial procedure (expert or non-expert) and blinded to the results of the previous intervention. | Diagnostic Test: HD- colonoscopy<br>* HD-colonoscopy performed by an expert or non-expert endoscopist. All lesions will be recorded, assessed, and removed for histological analysis.<br>Diagnostic Test: HD-colonoscopy assisted by AI<br>* HD-colonoscopy with AI-assisted polyp detector. New polyps detected by AI will be recorded, removed, and studied.<br>|
| Experimental: AI-HD colonoscopy + HD-colonoscopy<br>This group is comprised by patients >45 years of age submitted for diagnostic colonoscopy. In the same session a HD-colonoscopy assisted by artificial intelligence will be performed followed by an HD-colonoscopy alone.The second procedure will be performed by an operator with the same-level-of -expertise in comparison to the initial procedure (expert or non-expert) and blinded to the results of the previous intervention. | Diagnostic Test: HD- colonoscopy<br>* HD-colonoscopy performed by an expert or non-expert endoscopist. All lesions will be recorded, assessed, and removed for histological analysis.<br>Diagnostic Test: HD-colonoscopy assisted by AI<br>* HD-colonoscopy with AI-assisted polyp detector. New polyps detected by AI will be recorded, removed, and studied.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Adenoma detection rate (ADR) | The ADR will be determined by every new colonoscopy (second intervention) with at least one adenoma, histologically proven/NBI NICE classification. Results will be compared between experts and non-experts endoscopists. | up to one month | 
| Polyp detection rate (PDR) | The PDR will be determined by every new colonoscopy (second intervention) with at least one polyp. Results will be compared between experts and non-experts endoscopists. | up to two hours | 
| Diagnostic performance of AI-assisted polyp detector | The diagnostic performance of the AI-assisted system will be assessed by sensitivity, specificity, positive and negative predictive values (PPV and NPV) and observer agreement. | up to three years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Adenoma Miss Rate (AMR) | The AMR will be determined by the total number of missed adenomas on initial examination. The diagnosis of adenoma will be made by NBI NICE classification or biopsy. | Up to one month | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial intelligence, Colonoscopy, colorectal cancer