Multimodal Imaging Diagnosis and Decision Aid System for Hepatic Echinococcosis Based on Image Omics and Vision Macromodel

Hepatic echinococcosis (hepatic echinococcosis) is an important zoonotic disease widely existing in the agricultural and pastoral areas of northwest China. The disease can be parasitic in any part of the human body and may affect multiple organs. In severe cases, patients will lose the ability to work. At present, the disease faces challenges in diagnostic accuracy, specific type identification, preoperative activity assessment, postoperative recurrence prediction, and decision evaluation of T-tube indentation. This problem is particularly significant in high incidence areas with uneven distribution of medical resources and shortage of excellent imaging physicians and clinicians. Our previous studies have demonstrated that the use of visual large models and imaging omics algorithms can effectively segment liver echinococcus lesions, extract key features, and provide clinicians with accurate and reliable diagnosis and treatment recommendations. We believe that on the basis of the transformation of different medical image modes (such as MRI, CT and ultrasound) based on a broader multicentre large data set, the goal of effective identification, diagnosis, surgical decision support, and postoperative accurate prediction of hepatic echinococcosis can be achieved. We will use artificial intelligence technology solutions such as adversarial generation network, vision large model, image omics and decision level fusion, taking into account diagnosis and treatment efficiency, diagnosis and treatment automation and interpretability of diagnosis results, to build a comprehensive accurate diagnosis and prognosis system for hepatic echinococcosis