Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases

NCT ID: NCT04222439

Last Updated: 2020-02-18

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

100000 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-01-01

Study Completion Date

2020-02-29

Brief Summary

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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

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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.

Conditions

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Gastrointestinal Disease

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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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.

Group Type EXPERIMENTAL

AI for the Diagnosis of Gastrointestinal Diseases

Intervention Type DEVICE

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.

Interventions

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AI for the Diagnosis of Gastrointestinal Diseases

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

Eligibility Criteria

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Inclusion Criteria

* Participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals.

Exclusion Criteria

\-
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Shandong University

OTHER

Sponsor Role lead

Responsible Party

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Xiuli Zuo

director of Qilu Hospital gastroenterology department

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Xiuli Zuo, MD,PhD

Role: PRINCIPAL_INVESTIGATOR

Qilu Hospital of Shandong University

Locations

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Qilu Hospital, Shandong University

Jinan, Shandong, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Xiuli Zuo, MD,PhD

Role: CONTACT

15588818685

Facility Contacts

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Xiuli Zuo, PhD

Role: primary

15588818685 ext. 053188369277

Other Identifiers

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2019-SDU-QILU-G710

Identifier Type: -

Identifier Source: org_study_id

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