Clinical Trials Logo

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

NCT number NCT04811599
Other study ID # 2020-SDU-QILU-G056
Secondary ID
Status Enrolling by invitation
Phase
First received
Last updated
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
Health authority
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: -

Study Design


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
See also
  Status Clinical Trial Phase
Active, not recruiting NCT03566160 - Pilot Study for Evaluation of Cryobiopsy and Correlation With Standard Forceps Biopsy N/A
Active, not recruiting NCT05053191 - Advancing Nursing Practices in Hospital Oncology Care N/A
Recruiting NCT03602677 - Indocyanine Green Fluorescence Imaging in Prevention of Colorectal Anastomotic Leakage N/A
Recruiting NCT04138225 - The Ecological Role of Yeasts in the Human Gut
Completed NCT00671177 - Clinical Evaluation of Water Immersion Colonoscopy Insertion Technique N/A
Completed NCT00910104 - Cholestasis Reversal: Efficacy of IV Fish Oil Phase 2/Phase 3
Recruiting NCT06236594 - Application of Multimodal Endoscopic Functional Imaging Technology in the Diagnosis of Common Gastrointestinal Diseases
Completed NCT05409196 - Phase 1 Trial for Safety, Tolerability, and Immunogenicity of a Live, Attenuated, Oral Shigella/ETEC Combination Vaccine to Healthy Adults Phase 1
Enrolling by invitation NCT04719117 - Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety
Recruiting NCT03719209 - Using Virtual Reality for Patients With Gastrointestinal Disease N/A
Enrolling by invitation NCT03319446 - Collection of Anonymized Samples N/A
Enrolling by invitation NCT03234543 - Remote Ischemic Conditioning in Abdominal Surgery N/A
Completed NCT04498208 - Immune Modulation by Enhanced vs Standard Prehabilitation Program Before Major Surgery N/A
Completed NCT03549494 - Evaluation of Ocoxin®-Viusid® in Advanced Stomach Cancer and Gastric Esophagogastric Junction Phase 2
Completed NCT03559543 - Evaluation of Ocoxin®-Viusid® in Metastatic Colorectal Adenocarcinoma Phase 2
Completed NCT03723447 - Intraoperative TAP Block With Bupivacaine/Dexamethasone Against Liposomal Bupivacaine (Exparel®) Phase 4
Not yet recruiting NCT04514042 - Comparison of Zenker's Diverticulum Treatment Using Peroral Endoscopic Myotomy and Flexible Endoscopy Septotomy. N/A
Suspended NCT04519086 - The Optimization of a Low-dose CT Protocol in Patients With Suspected Uncomplicated Acute Appendicitis and BMI >30 N/A
Completed NCT03019042 - Efficacy and Safety of Hou Gu Mi Xi in Patients With Spleen Qi Deficiency and Non-organic Gastrointestinal Disorders N/A
Completed NCT05008640 - Creation of an E-toileting Log Through Classification of the Physical Properties of Stool and Urine Using TrueLoo™