Official Title: Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research
Status: RECRUITING
Status Verified Date: 2024-07
Last Known Status: None
Delayed Posting: No
If Stopped, Why?: Not Stopped
Has Expanded Access: No
If Expanded Access, NCT#: N/A
Has Expanded Access, NCT# Status: N/A
Acronym: PSLF
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 resultsIn 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 collectedThe 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