Prediction of Significant Liver Fibrosis

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

We intend to design a cloud platform with data storage, processing and analysis components.Select the appropriate technology stack to ensure that the platform has the ability to handle large-scale data, and has good scalability and performance.The previously built multimodal liver fibrosis precision typing model was then embedded into the platform, ensuring that the model could handle a variety of data types and integrate seamlessly with other components of the platform.At the same time, the stream processing technology is used to integrate the real-time monitoring and analysis function of the platform, so as to make rapid prediction and classification of the newly acquired liver fibrosis case data.The accuracy and stability of the model were further verified in the multi-center clinical data, and the multi-modal big data liver fibrosis early warning cloud platform system was built

To test the effectiveness of the multi-modal big data liver fibrosis warning platform system in a real multi-center clinical environment. We will also explore the possibility of using the platform for long-term follow-up of patients, remote testing of patients and regular assessment of disease progression