Prediction of Significant Liver Fibrosis

NCT06509230 | Recruiting

• 1 other identifier: KY2024072
• Study Type: observational
• Target: 700
• Locations: 1 country
• Sites: 1

Brief Summary

The deep learning method based on convolutional neural network (CNN) was used to extract the relevant features of liver fibrosis classification from the multi-modal information of digital pathological sections, clinical parameters and biomarkers of a large number of existing cases of liver puncture, and the U-Net architecture of CNN was used to segment and extract the features of clinical medical images.

Conditions

Liver Fibrosis

Keywords

Chronic hepatitis; Liver fibrosis; Artificial intelligence; Auxiliary diagnosis

Outcome Measures

Primary Outcomes (1)

• Model development

  Imaging (such as CT scan, MRI, X-ray, etc.) features and clinical parameters of patients were extracted, including population baseline characteristics (such as age, gender, comorbiditions, etc.), blood biochemical indicators (such as blood glucose, lipids, liver function indicators, etc.), and blood cytology indicators (such as white blood cell count, red blood cell count, etc.). Completed case selection and cohort establishment, multi-modal feature extraction and model development

  2024.6-2024.12

Secondary Outcomes (1)

• Build a multi-modal big data liver fibrosis early warning cloud platform system

  2025.1-2025.12

Other Outcomes (1)

• Evaluation Multi-modal big data liver fibrosis warning platform system effectiveness

  2026.1-2026.12

Study Arms (3)

mild fibrosis

S0-1

significant liver fibrosis

S2

Advanced liver fibrosis

S3-4

Eligibility Criteria

• Age: 18 Years - 60 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

A patient with chronic hepatitis B liver fibrosis confirmed by liver biopsy

You may qualify if:

• Age of 18-60 years old
• The diagnosis of chronic hepatitis B is in line with the diagnostic criteria of China's 2019 Chronic Hepatitis B Prevention and Treatment Guidelines, and the diagnosis of non-alcoholic fatty liver is in line with the Asian Pacific Hepatology Association guidelines
• Imaging showed no liver cancer

You may not qualify if:

• There are contraindications for liver biopsy
• Liver pathology did not meet the criteria

Sponsors & Collaborators

• Huang Haijun: lead
• East China University of Science and Technology: collaborator

Study Sites (1)

Haijun Huang

Hangzhou, Zhejiang, 310014, China

RECRUITING

MeSH Terms

Conditions

Liver Cirrhosis; Hepatitis, Chronic

Condition Hierarchy (Ancestors)

Liver Diseases; Digestive System Diseases; Fibrosis; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Hepatitis; Chronic Disease; Disease Attributes

Central Study Contacts

Haijun Huang

CONTACT

13758186635; huanghaijun0826@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Target Duration: 4 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Protomedicus

Study Record Dates

• First Submitted: July 10, 2024
• First Posted: July 19, 2024
• Study Start: July 20, 2024
• Primary Completion: December 31, 2024
• Study Completion (Estimated): December 31, 2026
• Last Updated: July 19, 2024

Record last verified: 2024-07

Locations

CN (1)