Hepatobiliary surgery treatment information sharing system and sharing method based on Internet

Abstract

The invention belongs to the technical field of hepatobiliary surgery treatment information sharing. The invention discloses a hepatobiliary surgery treatment information sharing system and sharing method based on Internet. The hepatobiliary surgery treatment information sharing system based on the Internet comprises a patient information acquisition module, an information processing module, a diagnosis module, a central control module, a treatment information generation module, a network communication module, an illness state prediction module, a prevention and control module, a database anda display module. According to the hepatobiliary surgery treatment information sharing system and sharing method based on Internet, benign and malignant lumps are identified by utilizing the classifier trained by a textural feature data set of the liver CT image through the diagnosis module, and the result is not influenced by human subjective factors, so that the human subjective factor influenceof pathological examination and other examinations is avoided, and the diagnosis accuracy is greatly improved; and meanwhile, by utilizing a prediction model based on a deep learning technology through the illness state prediction module, the problem that the small difference of the gene expression quantity is difficult to grasp due to subjectivity of people is solved, and positive significance is achieved for development of gene therapy of liver cancer.

Description

Technical field [0001] The invention belongs to the technical field of hepatobiliary surgery treatment information sharing, and in particular relates to an Internet-based hepatobiliary surgery treatment information sharing system and sharing method. Background technique [0002] Liver and gallbladder refers to the general term for liver and gallbladder. There are many variations in the cystic duct and its opening, and it is often easily damaged during surgery. However, the current diagnosis and treatment of liver and gallbladder have a high misdiagnosis rate and inaccurate diagnosis results; at the same time, they cannot accurately predict liver and gallbladder disease. Existing liver and gallbladder diagnosis methods use deep learning algorithms to extract image features and image texture features, and apply XGBoost algorithm to achieve intelligent diagnosis of diffuse liver diseases on ultrasound images. It is an application of artificial intelligence methods in ultrasound ima...

AI Technical Summary

Problems solved by technology

[0004] (1) The existing hepatobiliary diagnosis and treatment have a high rate of misdiagnosis and inaccurate diagnostic results;

[0005] (2) Unable to accurately predict the liver and gallbladder disease;

[0006] (3) The patient needs to be re-examined every time he changes the hospital, and the previous inspection information cannot be shared;

[0007] (4) There are no security protection measures for the network interface of the existing medical system, and the privacy of patients is not protected

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