Clinical trial NCT05979935: Tongue Images-based AI Deep Learning Model for the Prediction of Esophageall Varies in Liver Cirrhosis
Official title: Tongue Images-based AI Deep Learning Model for the Prediction of Esophageal Varices in Liver Cirrhosis Patients
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Esophageal and Gastric Varices
Keywords: tongue bioinformatics, esophageal and gastric varices
Lead sponsor: Qilu Hospital of Shandong University
Sponsor class: OTHER
Collaborators: name: Chinese PLA General Hospital
name: Qianfoshan Hospital
name: Shandong Provincial Hospital
name: Shandong Public Health Clinical Center
name: Jinan Central Hospital
First posted: 2023-08-07
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The subjects in this study are patients with liver cirrhosis from the Department of Gastroenterology, Qilu Hospital, Shandong University, the Department of Hepatology, Shandong Provincial Qianfoshan Hospital, and the Department of Gastroenterology, Jinan Central Hospital, Shandong Provincial Hospital and 960th Hospital of PLA. According to the 《Prevention and Treatment Plan for Viral Hepatitis》 revised by the Infectious Diseases and Parasitic disease Branch and Hepatology Branch of the Chinese Medical Association in September 2000, Patients with liver cirrhosis and esophageal varices aged 18-75 years will be included in the study.

Brief summary: The goal of this observational study is to compare the tongue images of liver cirrhosis patients with high-risk and low-risk esophageal varies.

The main question of this study is to explore whether there exists a relationship between the degree of esophageal varices and tongue bioinformatics in liver cirrhosis. it aims to answer 2 questions as below:

question 1: If these is a significant difference in tongue images between patients in liver cirrhosis with high-risk and low-risk esophageal varices.

Question 2; If tongue bioinformatics could be used as a diagnostic method for prediction of esophageal varices in liver cirrhosis.

Detailed description: Firstly, participants will be divided into two groups according to their degree of esophageal varices from endoscopic examination and CT report, including high-risk group and low-risk group. Secondly, participants will be asked to show their tongue, including the surface and sublingual veins of tongue, and the tongue images of each participants will be collected by researchers via camera. After finishing tongue image collection, participants will receive a professional tongue diagnosis report in Traditional Chinese Medicine and health suggestion. Finally, tongue images will be analyzed by using AI deep learning model for information extraction.

Eligibility criteria: Inclusion Criteria:

1. Patients who were diagnosed with liver cirrhosis;
2. Patients who received abdominal enhanced CT scans and upper endoscopic examination;
3. Patients who signed informed consent;

Exclusion Criteria:

1. Patient who has previously received transjugular intrahepatic portosystemic shunt (TIPS), splenectomy, Hepatectomy, balloon occlusion of retrograde veins or liver transplantation;
2. Patient with liver cancer;

(4) Patients with hypertension, cor pulmnale, emphysema, heart failure and other severe systemic diseases;

Primary outcomes:
measure: Endoscopic examination is used to screen esophageal varices for liver cirrhosis patients.
timeFrame: 6 months
measure: Tongue images analysis by AI deep learning model
timeFrame: 6 months
Secondary outcomes:
measure: Specific Characteristic in Tongue Bioinformatics
timeFrame: 6 months
Other outcomes:
Locations:
city: Jinan
country: China
facility: Qilu Hospital of Shdong University
state: Shandong
Study officials:
affiliation: Qilu Hospital of Shandong University
name: Yanjing Gao, PhD MD
role: STUDY_CHAIR
References:
citation: Ou J, Li R, Zeng R, Wu CQ, Chen Y, Chen TW, Zhang XM, Wu L, Jiang Y, Yang JQ, Cao JM, Tang S, Tang MJ, Hu J. CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study. Cancer Imaging. 2019 Oct 16;19(1):66. doi: 10.1186/s40644-019-0254-0.
citation: Tandon M, Singh H, Singla N, Jain P, Pandey CK. Tongue thickness in health vs cirrhosis of the liver: Prospective observational study. World J Gastrointest Pharmacol Ther. 2020 Aug 8;11(3):59-68. doi: 10.4292/wjgpt.v11.i3.59.
citation: He C, Liao Q, Fu P, Li J, Zhao X, Zhang Q, Gui Q. Microbiological characteristics of different tongue coatings in adults. BMC Microbiol. 2022 Sep 9;22(1):214. doi: 10.1186/s12866-022-02626-7.
citation: Lin Y, Li L, Yu D, Liu Z, Zhang S, Wang Q, Li Y, Cheng B, Qiao J, Gao Y. A novel radiomics-platelet nomogram for the prediction of gastroesophageal varices needing treatment in cirrhotic patients. Hepatol Int. 2021 Aug;15(4):995-1005. doi: 10.1007/s12072-021-10208-4. Epub 2021 Jun 11.