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.
Eligibility
Sex
ALL
Min age
18 Years
Max age
60 Years
Healthy volunteers
Accepted
Inclusion Criteria:
1. Age of 18-60 years old
2. 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
3. Imaging showed no liver cancer
Exclusion Criteria:
1. There are contraindications for liver biopsy
2. Liver pathology did not meet the criteria
Primary outcome measure(s)
Model development — 2024.6-2024.12 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
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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