Deep-learning-based cholelithiasis intelligent auxiliary system

Abstract

The invention discloses a deep-learning-based cholelithiasis intelligent auxiliary system. The system comprises a cloud server and a client which are in bidirectional connection, and the cloud servercomprises a cloud platform image database, a cloud platform algorithm module, an information acquisition module, a comprehensive module and a deep learning analysis module. Accordingly, bidirectionalconnection between the cloud server and the client is set, and the cloud server comprises a cloud platform image database, a cloud platform algorithm module, an information acquisition module, a comprehensive module and a deep learning analysis module, automatic extraction and intelligence for chololithiasis can be achieved; not only can self-analysis, self-identification and result self-generation be achieved, doctors in the hepatology department can be helped to perform and learn, and the clinical reference is provided for related institutions or designated hospitals, but also user data andrecords can serve as historical reference data to be stored in the database so as to serve as cholelithiasis clinical history reference of patients.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to an intelligent assistance system for cholelithiasis based on deep learning. Background technique [0002] Cholelithiasis, also known as gallstones, refers to the disease of the biliary tract system, including the occurrence of stones in the gallbladder or bile ducts. The main causes of gallstones may be quiet and inactive, obesity, skipping breakfast, snacks after meals, liver cirrhosis, and genetic factors. Biliary tract infection is a common disease. It is divided into cholecystitis and cholangitis according to the location of the disease. After the formation of gallstones in the gallbladder, it can stimulate the gallbladder mucosa, which can not only cause chronic inflammation of the gallbladder, but also cause gallstones incarcerated in the neck of the gallbladder or gallbladder. After the tube, it can also cause secondary infection, leading to acute inflammation of the gall...

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Problems solved by technology

[0003] There are many kinds of gallstone diseases, the pathogenic factors are intricate, and they have the characteristics of high incidence rate and difficulty in dissolving and expulsing stones. In addition, there are various types and forms of gallstone diseases, and the lesion shapes of some gallstone diseases are also very similar, which greatly hinders the treatment of gallstones. The correct diagnosis and treatment of gallstone disease, in this case, some newly graduated hepatobiliary physicians need to study for a long time to be able to master the skills of gallstone disease proficiently, which brings a huge challenge to the clinical practice of hepatobiliary physicians, for this reason, We propose a deep learning-based intelligent assistance system for cholelithiasis

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