Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Shanghai Changzheng Hospital | OTHER |
| Shanghai East Hospital of Tongji University | OTHER |
| Eighth Affiliated Hospital, Sun Yat-sen University | OTHER |
Not provided
Not provided
Not provided
Not provided
The aim of this observational study is to establish an AI deep learning model that can dianosie high-risk varices for patients with cirrhosis effeciently.
The main question of this study is to esplore:
question 1: Developing a digital tongue diagnosis model, specifically a deep learning model to diagnose high-risk esophageal and gastric varices (HRV) associated with cirrhosis using sublingual vein images. Answering the question of whether the new tongue diagnosis method can accurately diagnose.
Question 2: Compare the diagnostic efficacy digital tongue diagnosis model with diagnostic models constructed using other biochemical indicators for HRV in cirrhosis, and answer the question of "how to use it optimally."
Question 3: Exploring the correlation between sublingual vein characteristics and Hepatic venous pressure gradient (HVPG).
Question 4: Compared with endoscopic examination results, validate the diagnostic performance of the model (AUC ≥ 0.90) and screen for key parameters of sublingual vein characteristics (such as sublingual vein varicosity diameter, vein length, color, etc.).
Question 5: Follow-up tongue examination images of patients with cirrhosis who underwent treatment (e.g., endoscopy, splenic embolization, TIPS, etc.) at 1, 2, and 3 years post-treatment were evaluated to assess the efficacy of digital tongue examination models in predicting high-risk esophageal and gastric variceal bleeding at 1, 2, and 3 years post-treatment, as well as the efficacy in predicting endoscopic treatment failure rates and patient mortality associated with bleeding.
Firstly, participants will be divided into two groups according to their degree of esophageal varices from endoscopic examination and CT report, including high-risk varices (HRV) group and low-risk varices (LRV) 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 AI deep learning model and some specifialized information will be estracted exactly. Besides, 60 patietns will be selected to eplore whtether sublingual vein characteristics is assoicated with HVPG. Finally, patietns will be followed up during 3 years, and gastric variceal bleeding, endoscopic treatment failure rates, and patient mortality risks related to bleeding will be analyzed.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| patients with high-risk of esophageal varices in liver cirrhosis | The maximum diameter of varices ≥ 5 mm or maximum diameter of varices <5mm with positive red sign in patients with liver cirrhosis |
| |
| patients with low-risk of esophageal varices in liver cirrhosis | the maximum diameter of esophageal varices < 5 mm without positive red sign in patients with liver cirrhosis. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| tongue diagnosis | Diagnostic Test | The tongue image of participants will be collected via camera, and tongue images will be used for AI deep model learning analysis. |
|
| Measure | Description | Time Frame |
|---|---|---|
| AUC of tongue diagnostic model | Using endoscopic diagnostic criteria as the "gold standard," we calculated the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value of the VIT-based digital tongue diagnosis model to evaluate its diagnostic performance in diagnosing HRV in cirrhosis. | through study completion, up to 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| ROC of biochemical characteristic | Calculate the AUC valthe digital tongue diagnosis model.ue, sensitivity, specificity, positive predictive value, negative predictive value, and other relevant parameters of the ROC curve for the HRV diagnostic model for cirrhosis constructed based on biochemical indicators, and compare them with | through study completion, up to 3 years |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
The subjects in this study are patients with liver cirrhosis from the Department of Gastroenterology, Qilu Hospital, Shandong University and other 15 hospitaals. 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.
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Yanjing Gao, PhD MD | Qilu Hospital of Shandong University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Qilu Hospital of Shdong University | Jinan | Shandong | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31619297 | Result | 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. | |
| 32844044 | Result |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D004932 | Esophageal and Gastric Varices |
| D008103 | Liver Cirrhosis |
| D006975 | Hypertension, Portal |
| ID | Term |
|---|---|
| D004935 | Esophageal Diseases |
| D005767 | Gastrointestinal Diseases |
| D004066 | Digestive System Diseases |
| D008107 | Liver Diseases |
Not provided
Not provided
| Meng Chao Hepatobiliary Hospital of Fujian Medical University |
| OTHER |
| Tianjin Medical University General Hospital | OTHER |
| Army Medical Center of PLA | OTHER_GOV |
| Shandong Provincial Hospital | OTHER_GOV |
| Qianfoshan Hospital | OTHER |
| Shandong Public Health Clinical Center | OTHER_GOV |
| 960th Hospital of Joint Logistics Support Force of People's Liberation Army of China | OTHER |
| Jinan Central Hospital | OTHER |
| Weifang People's Hospital | OTHER |
| Liaocheng People's Hospital | OTHER |
| The Second Affiliated Hospital of Shandong First Medical University | OTHER |
| Jining First People's Hospital | OTHER |
Not provided
Not provided
Not provided
| Association between HVPG and sublingual vein | The association between sublingual vein and HVPG | through study completion, up to 3 years |
| Characteristic of sublingual vein | Key parameters for screening sublingual vein characteristics (such as sublingual vein varicosity diameter, vein length, color, etc.). | through studyy completion, up to 3 years |
| The rate of esophageal variceal bleeding, endoscopic treatment failure, and patient mortality | The incidence of esophageal variceal bleeding, endoscopic treatment failure rate, and patient mortality related to bleeding in patients with cirrhosis 1, 2, and 3 years after diagnosis. | through study completion, up to 3 years |
| 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. |
| 36085010 | Result | 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. |
| 34115257 | Result | 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. |
| 36174643 | Result | Gralnek IM, Camus Duboc M, Garcia-Pagan JC, Fuccio L, Karstensen JG, Hucl T, Jovanovic I, Awadie H, Hernandez-Gea V, Tantau M, Ebigbo A, Ibrahim M, Vlachogiannakos J, Burgmans MC, Rosasco R, Triantafyllou K. Endoscopic diagnosis and management of esophagogastric variceal hemorrhage: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy. 2022 Nov;54(11):1094-1120. doi: 10.1055/a-1939-4887. Epub 2022 Sep 29. |
| 35120736 | Result | de Franchis R, Bosch J, Garcia-Tsao G, Reiberger T, Ripoll C; Baveno VII Faculty. Baveno VII - Renewing consensus in portal hypertension. J Hepatol. 2022 Apr;76(4):959-974. doi: 10.1016/j.jhep.2021.12.022. Epub 2021 Dec 30. |
| D005355 |
| Fibrosis |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |