Deep Learning Algorithm for Recognition of Colonic Segments.

Study Overview
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Brief Summary
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The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.

Detailed Description
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Colonoscopy is recommended as a routine examination for colorectal cancer screening. Complete inspection of all colon segments is the basis of colonoscopy quality control, and furthermore improves the detection rates of small adenomas. Recently, deep learning algorithm based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. However, there is still a blank in recognition of anatomic sites, which restricts the realization of AI-aided lesions detection and disease severity scoring. This study aim to train an algorithm to recognize key colonic segments, and testify the accuracy of each segments recognition as compared to endoscopic physicians.

Official Title
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Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.

Conditions
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Colonic Diseases

Intervention / Treatment
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* Device: AI assisted recognition of colonic segments


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients aged 18-70 years undergoing conventional colonoscopy Exclusion Criteria: Known or suspected bowel obstruction, stricture or perforation Compromised swallowing reflex or mental status Severe chronic renal failure(creatinine clearance < 30 ml/min) Severe congestive heart failure (New York Heart Association class III or IV) Uncontrolled hypertension (systolic blood pressure > 170 mm Hg, diastolic blood pressure > 100 mm Hg) Dehydration Disturbance of electrolytes Pregnancy or lactation Hemodynamically unstable Unable to give informed consent

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 70 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: Health Services Research
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: AI monitoring colonoscopy<br>Patients in this group go through colonoscopy under the AI monitoring device. | Device: AI assisted recognition of colonic segments<br>* After receiving standard bowel preparation regimen, patients go through colonoscopy under the AI monitoring device. The whole withdrawal process is monitored by AI associated recognition system. Key colonic segments include ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum. When typical anatomic sites are detected, the AI device will automatically captured relevant images and report the name of each segment on the screen. The operating endoscopy expert will give the final answer and judge the performance of AI, which is set as a golden standard. Then all the AI captured images will be reviewed by human group, which consists of three to five experienced endoscopic physicians.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The accuracy of each colonic segment real-time recognition with deep learning algorithm. | The segmental recognition accuracy is the proportion of correctly recognized segments divided by the number of involved patients. The accuracy rate of ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum will be separately calculated. | 3 months. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group. | The total recognition accuracy is the proportion of correctly recognized images divided by the number of AI captured images. Then all AI captured images will be reviewed by experts group to give a human evaluating rate. Two rates will be compared by student t test to analyze the difference. | 3 months. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Deep learning, Colonoscopy, Central Neural Networks