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

NCT ID: NCT04811599

Last Updated: 2021-03-23

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-03-21
• Study Completion Date: 2022-06-01

Brief 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.

Detailed 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.

Conditions

Gastrointestinal Disease

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Study Groups

deep learning algorithm group

Before patients going through colonoscopy or gastroscopy ,taking them tongue images and collecting basic information by mobile phone with Anymed.After examination,endoscopic report and histology analysis is collected .Categorizing the images by gastrointestinal diseases，developing and validating a deep learning algorithm for the diagnosis of digestive tract diseases depending on tongue images.Extracting tougue coating,gastric mucosa and stool DNA by high-throughput sequencing,and analyzing their composation,adundance and diversity.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

-

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Shandong University

OTHER

Sponsor Role: lead

Responsible Party

Xiuli Zuo

Director of Qilu Hospital gastroenterology department

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Xiuli Zuo, MD,PhD

Role: STUDY_CHAIR

Study Principal investigator

Locations

Qilu Hospital, Shandong University

Jinan, Shandong, China

Countries

China

Other Identifiers

2020-SDU-QILU-G056

Identifier Type: -

Identifier Source: org_study_id