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

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04811599

Enrollment

2000

Registered

2021-03-23

Start date

2021-03-21

Completion date

2022-06-01

Last updated

2021-03-23

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

Conditions

Gastrointestinal Disease

Keywords

Gastrointestinal Disease, tongue image, artificial intelligence, deep learning, gastrointestinal flora

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.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

Yes

Inclusion criteria

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

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	1 month	The diagnostic specificity of gastrointestinal diseases with deep learning algorithm

Countries

China

Outcome results