NCT06509230

Prediction of Significant Liver Fibrosis

Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 700 (estimated)
• Sponsor: Huang Haijun · Academic / Other
• Sex: All
• Age: 18 Years – 60 Years
• Healthy volunteers: Accepted

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.

Conditions

• Liver Fibrosis

Interventions

Type	Name	Description
OTHER	—	The fibrosis grades were grouped without drug intervention

Timeline

• Start date: 2024-07-20
• Primary completion: 2024-12-31
• Completion: 2026-12-31
• First posted: 2024-07-19
• Last updated: 2024-07-19

Locations

1 site across 1 country: China