Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
Development and Validation of a Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Locations
-
-
Shandong
-
Jinan, Shandong, China, 250012
- Recruiting
- Qilu Hospital, Shandong University
-
Contact:
- Xiuli Zuo, PhD
- Phone Number: 053188369277 15588818685
- Email: zuoxiuli@sdu.edu.cn
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals.
Exclusion Criteria:
-
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: AI monitoring gastrointestinal endoscopy
After receiving standard preparation regimen, patients go through colonoscopy or gastroscopy under the AI monitoring device.
The whole procedure is monitored by AI associated recognition system.
Gastrointestinal diseases will be detect and diagnosis in which the AI device will automatically captured relevant images and report the site of each segment on the screen.
Histology analysis 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.
|
After receiving standard preparation regimen, patients go through colonoscopy or gastroscopy under the AI monitoring device.
The whole procedure is monitored by AI associated recognition system.
Gastrointestinal diseases will be detect and diagnosis in which the AI device will automatically captured relevant images and report the site of each segment on the screen.
Histology analysis 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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Time Frame: 1 month
|
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
|
1 month
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
Time Frame: 1 month
|
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
|
1 month
|
|
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Time Frame: 1 month
|
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
|
1 month
|
|
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm.
Time Frame: 1 month
|
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
|
1 month
|
|
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm.
Time Frame: 1month
|
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
|
1month
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2019-SDU-QILU-G710
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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