Cerebral apoplexy type prediction method and device

Abstract

The invention provides a cerebral apoplexy type prediction method and device; the method comprises the following steps: obtaining brain sample images of a plurality of cerebral apoplexy patients, and obtaining focus information corresponding to each brain sample image; using a machine learning method to describe the brain sample image features according to the focus information, thus forming a cerebral apoplexy focus information data model; obtaining a brain scan image, and using the cerebral apoplexy focus information data model to predict the cerebral apoplexy type of the brain scan image. The method firstly uses big data to build he cerebral apoplexy focus information model; in determination, the method needs to directly input the patient brain scan image into the model, and uses the model to calculate the brain scan image, thus finally outputting the cerebral apoplexy type; the method is fast in response speed, hard to cause condition delay, needs no artificial participation in the whole process, thus reducing doctor medical pressure; in addition, the method and device cannot have inaccurate condition determination caused by personal differences.

Description

technical field [0001] The present invention relates to the technical field of image processing, and in particular, to a stroke type prediction method and device. Background technique [0002] Stroke is the scientific name of stroke, which is a sudden onset of cerebral blood circulation disorders, also known as cerebrovascular accident. It refers to the patients with cerebrovascular disease, due to various predisposing factors to cause cerebral artery stenosis, occlusion or rupture, resulting in acute cerebral blood circulation disorder, clinically manifested as symptoms and signs of one-time or permanent brain dysfunction. Stroke is divided into ischemic stroke and hemorrhagic stroke. Stroke is characterized on cerebral angiography by narrowing, occlusion, or twisting of cerebral arteries. [0003] With the continuous development and maturity of computer technology and clinical diagnostic technology, DICOM (Digital Imaging and Communications in Medicine, medical digital i...

AI Technical Summary

Problems solved by technology

However, there is a large time difference between the patient's cerebral angiography and the doctor's obtaining of the DICOM image data. It takes a lot of time to manually calibrate the measurement indicators and manually judge the measurement indicators to diagnose stroke. If it is too long, it is easy to cause the condition to be delayed, and an experienced doctor is needed for the diagnosis, and it is easy to cause inaccurate judgment of the condition due to human differences

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