Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases Depending on Tongue Images

Gastrointestinal Disease Clinical Trial

Official title:

Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases Depending on Tongue Images

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT04811599
• Other study ID #: 2020-SDU-QILU-G056
• Status: Enrolling by invitation
• Start date: March 21, 2021
• Est. completion date: June 1, 2022

Study information

• Verified date: March 2021
• Source: Shandong University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The purpose of this study is to analysize the relationship between the characteristics of tongue image and the diagnosis of gastrointestinal diseases , then develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases depending on tongue images, so as to improve the objectiveness and intelligence of tongue diagnosis. At the same time, gastrointestinal flora of common tongue images were analyzed in order to provide a microecological basis for understanding the relationship between tongue images and digestive tract diseases.

Description:

Tongue diagnosis is an important part of traditional Chinese medicine.According to traditional Chinese medicine theory,health condition can assessed by observing tougue features,including color, gloss, shape and coating of the tongue, tongue features reflect gastric mucosal state, disease classification and prognosis. Recently, deep learning based on central neural networks (CNN) has shownTongue diagnosis is an important part of traditional Chinese medicine.According to traditional Chinese medicine theory,health condition can assessed by observing tougue features,including color, gloss, shape and coating of the tongue, tongue features reflect gastric mucosal state, disease classification and prognosis. Recently, deep learning based on central neural networks (CNN) has shown multiple potential in detecting and diagnosing gastrointestinal diseases. However, there is still a blank in recognition of gastrointestinal diseases .This study aims to develop and validate a deep learning algorithm for the diagnosis of digestive tract diseases depending on tongue images,and analyze gastrointestinal flora of common tongue images.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 2000
• Est. completion date: June 1, 2022
• Est. primary completion date: June 1, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

• Patients aged 18 - 80 years undergoing endoscopic examination;patients gave informed consent and signed informed consent. Exclusion Criteria: -

Related Conditions & MeSH terms

• Digestive System Diseases
• Gastrointestinal Disease
• Gastrointestinal Diseases

Locations

Country	Name	City	State
China	Qilu Hospital, Shandong University	Jinan	Shandong

Sponsors (1)

Lead Sponsor	Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm	The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.	1 month
Secondary	The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm	The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.	1 month
Secondary	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm	1 month
Secondary	The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm	1 month
Secondary	The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm	1 month