Colonic Diseases Clinical Trial
Official title:
Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.
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.
Status | Not yet recruiting |
Enrollment | 60 |
Est. completion date | December 15, 2019 |
Est. primary completion date | November 15, 2019 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years to 70 Years |
Eligibility |
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 |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
---|---|
Shandong University |
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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 | 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. |
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