Deep Learning Algorithm for Recognition of Colonic Segments.

Not Applicable

• Conditions: Colonic Diseases
• Interventions: Device: AI assisted recognition of colonic segments

• Registration Number: NCT04087824
• Lead Sponsor: Shandong University

Brief Summary

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

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.

Study Timeline

• Status Verified: 2019-09
• Study First Submitted: 2019-09-11
• Study First Submitted QC: 2019-09-11
• Last Update Submitted: 2019-09-11
• Study First Posted: 2019-09-12
• Last Update Posted: 2019-09-12
• Study Start: 2019-09-15
• Primary Completion: 2019-11-15
• Study Completion: 2019-12-15

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 60

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

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
AI monitoring colonoscopy	AI assisted recognition of colonic segments	Patients in this group go through colonoscopy under the AI monitoring device.

Primary Outcome Measures

Name	Time	Method
The accuracy of each colonic segment real-time recognition with deep learning algorithm.	3 months.	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.

Secondary Outcome Measures

Name	Time	Method
The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group.	3 months.	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.