AI-assisted Detection of Missed Colonic Polyps

Study Overview
=================
Brief Summary
-----------------
A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps

Detailed Description
-----------------
Consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate. Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses. Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded. The primary endoscopist conducted the colonoscopic examination in the usual manner. All colonoscopy procedures were performed with high-definition colonoscopes (EVIS-EXERA 290 video system, Olympus Optical, Tokyo, Japan). The colonoscopy was first advanced to the cecum in all patients as confirmed by identification of the appendiceal orifice and ileocecal valve or by intubation of the ileum. After cecal intubation, the colonoscopy was slowly withdrawn to the rectum by the primary endoscopist. The AI real time detection was then activated with the output displayed in a different monitor and was only viewed by an independent investigator, who was an experienced endoscopist. The primary endoscopist was blinded to the AI real time detection result al. The colon was divided into three segments during the examination: right side, transverse and left side colon, using hepatic flexure and splenic flexure as dividing landmark. All polyps were marked for size (measured with biopsy forceps), location and morphology according to the Paris classification, and then removed or biopsied for histological examination. After examination of each segment, segmental unblinding of the AI results were provided by the independent viewer. If additional polyps were detected by AI but not by the endoscopist, that segment were reexamined to look for the missed polyp. If no additional polyp was detected by the AI, the next colonic segment was examined. Missed lesions were defined as lesions identified by AI and then confirmed on reexamination by the endoscopist. The first withdrawal time (minus the polypectomy site) was measured. The Boston Bowel Preparation Scale score (BPPS) was used for evaluation of bowel cleanliness.

Official Title
-----------------
Artificial Intelligence-Assisted Real-time Detection of Missed Lesions During Colonoscopy: A Prospective Study

Conditions
-----------------
Colon Adenoma, Colonic Polyp, Colon Cancer

Intervention / Treatment
-----------------
* Device: Artificial intelligence-Assisted real time colonoscopy


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate Exclusion Criteria: Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses. Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.

Ages Eligible for Study
-----------------
Minimum Age: 40 Years
Maximum Age: 90 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Artificial intelligence-Assisted real time colonoscopy<br>AI assisted real-time detection of colonic lesions | Device: Artificial intelligence-Assisted real time colonoscopy<br>* The colonoscopy was performed under artificial intelligence assistance<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Adenoma miss rate | The number of patient had at least one missed adenoma | During the colonoscopy procedure | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Total number of adenoma missed | The total number of missed polyps for all subjects | During the colonoscopy procedure | 
| Colonic polyp miss rate | The number of patient had at least one missed adenoma | During the colonoscopy procedure | 
| Total number of missed polyps | The total number of missed polyps for all subjects | During the colonoscopy procedure | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Artificial intelligence, Colonoscopy, Deep learning, Detection, Computer-aided