Back to resultsDeep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
Primary Purpose
Gastrointestinal Disease
Status
Unknown status
Phase
Not Applicable
Locations
China
Study Type
Interventional
Intervention
AI for the Diagnosis of Gastrointestinal Diseases
Sponsored by
About this trial
This is an interventional diagnostic trial for Gastrointestinal Disease focused on measuring Deep Learning, Central Neural Networks, Endoscopy, Gastrointestinal Disease
Eligibility Criteria
Inclusion Criteria:
- Participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals.
Exclusion Criteria:
-
Sites / Locations
- Qilu Hospital, Shandong UniversityRecruiting
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
AI monitoring gastrointestinal endoscopy
Arm Description
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.
Outcomes
Primary Outcome Measures
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Secondary Outcome Measures
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Full Information
1. Study Identification
Unique Protocol Identification Number
NCT04222439
Brief Title
Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
Official Title
Development and Validation of a Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
Study Type
Interventional
2. Study Status
Record Verification Date
February 2020
Overall Recruitment Status
Unknown status
Study Start Date
January 1, 2020 (Actual)
Primary Completion Date
February 2020 (Anticipated)
Study Completion Date
February 2020 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Shandong University
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes
5. Study Description
Brief Summary
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
Detailed Description
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 all gastrointestinal diseases. This study aim to develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Gastrointestinal Disease
Keywords
Deep Learning, Central Neural Networks, Endoscopy, Gastrointestinal Disease
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
100000 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
AI monitoring gastrointestinal endoscopy
Arm Type
Experimental
Arm Description
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.
Intervention Type
Device
Intervention Name(s)
AI for the Diagnosis of Gastrointestinal Diseases
Intervention Description
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.
Primary Outcome Measure Information:
Title
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Description
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Time Frame
1 month
Secondary Outcome Measure Information:
Title
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
Description
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
Time Frame
1 month
Title
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Description
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Time Frame
1 month
Title
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm.
Description
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Time Frame
1 month
Title
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm.
Description
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Time Frame
1month
10. Eligibility
Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria:
Participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals.
Exclusion Criteria:
-
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Xiuli Zuo, MD,PhD
Phone
15588818685
Email
zuoxiuli@sdu.edu.cn
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Xiuli Zuo, MD,PhD
Organizational Affiliation
Qilu Hospital of Shandong University
Official's Role
Principal Investigator
Facility Information:
Facility Name
Qilu Hospital, Shandong University
City
Jinan
State/Province
Shandong
ZIP/Postal Code
250012
Country
China
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Xiuli Zuo, PhD
Phone
15588818685
Ext
053188369277
Email
zuoxiuli@sdu.edu.cn
12. IPD Sharing Statement
Learn more about this trial
Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
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