Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases

Development and Validation of a Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases

Status

UNKNOWN

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT04222439

Enrollment

100000

Registered

2020-01-10

Start date

2020-01-01

Completion date

2020-02-29

Last updated

2020-02-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gastrointestinal Disease

Keywords

Deep Learning, Central Neural Networks, Endoscopy, Gastrointestinal Disease

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.

Interventions

DEVICEAI 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.

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Yes

Inclusion criteria

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

Exclusion criteria

Design outcomes

Primary

Measure	Time frame	Description
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.	1 month	The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.

Secondary

Measure	Time frame	Description
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.	1 month	The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.	1 month	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm.	1 month	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm.	1month	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.

Countries

China

Contacts

Primary ContactXiuli Zuo, MD,PhD

zuoxiuli@sdu.edu.cn15588818685

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026