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Deep 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
Shandong University
About
Eligibility
Locations
Arms
Outcomes
Full info

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

18 Years - undefined (Adult, Older Adult)All SexesAccepts Healthy Volunteers

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

First Posted
January 7, 2020
Last Updated
February 14, 2020
Sponsor
Shandong University
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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

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Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases

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