Actively Recruiting
Prediction of Significant Liver Fibrosis
Led by Huang Haijun · Updated on 2024-07-19
700
Participants Needed
1
Research Sites
127 weeks
Total Duration
On this page
Sponsors
H
Huang Haijun
Lead Sponsor
E
East China University of Science and Technology
Collaborating Sponsor
AI-Summary
What this Trial Is About
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
Official Title
Prediction of Significant Liver Fibrosis
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Age of 18-60 years old
- Diagnosed with chronic hepatitis B according to China's 2019 guidelines
- Diagnosed with non-alcoholic fatty liver according to Asian Pacific Hepatology Association guidelines
- Imaging shows no liver cancer
You will not qualify if you...
- Presence of contraindications for liver biopsy
- Liver pathology does not meet required criteria
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Haijun Huang
Hangzhou, Zhejiang, China, 310014
Actively Recruiting
Research Team
H
Haijun Huang
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
3
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