Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Colonic Diseases
- Sponsor
- Shandong University
- Enrollment
- 60
- Primary Endpoint
- The accuracy of each colonic segment real-time recognition with deep learning algorithm.
- Last Updated
- 6 years ago
Overview
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.
Investigators
Xiuli Zuo
director of Qilu Hospital gastroenterology department
Shandong University
Eligibility Criteria
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
Outcomes
Primary Outcomes
The accuracy of each colonic segment real-time recognition with deep learning algorithm.
Time Frame: 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 Outcomes
- The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group.(3 months.)