Clinical trial NCT06509230: Prediction of Significant Liver Fibrosis
Official title: Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Liver Fibrosis
Keywords: Chronic hepatitis
Liver fibrosis
Artificial intelligence
Auxiliary diagnosis
Lead sponsor: Zhejiang Provincial People's Hospital
Sponsor class: OTHER
Collaborators: name: East China University of Science and Technology
First posted: 2024-07-19
Enrollment: 700
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: A patient with chronic hepatitis B liver fibrosis confirmed by liver biopsy

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.

Detailed description: Patients with chronic hepatitis B underwent B-ultrasound-guided liver biopsy, and were divided into mild liver fibrosis group (fibrosis grade 0-1, S1), significant liver fibrosis group (fibrosis grade 2, S2), advanced liver fibrosis group and early cirrhosis group (fibrosis grade 3-4, S3-4) according to the pathological results.In this study, 200 patients with different degrees of liver fibrosis and 200 normal volunteers were collected from 2018 to 2022, and their clinical biochemical data, imaging data and peripheral blood samples were collected.The pathological microenvironment characteristics, imaging characteristics, clinical parameter characteristics and other data of patients were extracted, and the distillation learning method based on teacher-student model was adopted to develop and construct a multi-modal big data analysis model for accurate grading of liver fibrosis, so as to achieve a non-invasive intelligent grading diagnosis system for liver fibrosis.

Eligibility criteria: 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 outcomes:
measure: Model development
timeFrame: 2024.6-2024.12
Secondary outcomes:
measure: Build a multi-modal big data liver fibrosis early warning cloud platform system
timeFrame: 2025.1-2025.12
Other outcomes:
measure: Evaluation Multi-modal big data liver fibrosis warning platform system effectiveness
timeFrame: 2026.1-2026.12
Locations:
city: Hangzhou
country: China
facility: Haijun Huang
state: Zhejiang
Study officials:
References: