Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 700
- 试验地点
- 1
- 主要终点
- Model development
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age of 18-60 years old
- •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
- •Imaging showed no liver cancer
排除标准
- •There are contraindications for liver biopsy
- •Liver pathology did not meet the criteria
结局指标
主要结局
Model development
时间窗: 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
次要结局
- Build a multi-modal big data liver fibrosis early warning cloud platform system(2025.1-2025.12)
研究者
Huang Haijun
Protomedicus
Zhejiang Provincial People's Hospital