Alzheimer's disease detection device and computer equipment

A technology to be detected and a detection model, applied in the field of medical disease detection, can solve the problems of inaccurate diagnosis results, local image influence of the diagnosis effect, and inability to fully express the real state of the human brain, so as to reduce the effect of manual participation.

Active Publication Date: 2020-03-27
SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The technical effect of this patented technology allows for accurate analysis of neurology data from brains obtained by analyzing MCI patients' images taken during clinic visits without having them undergo invasive procedures such as biopsies. By creating an artificial neural net (ANN) based on these Brain Network Construction Modules(BNCM), we can predict which parts of our brains will have specific characteristics associated therewith when they were affected by certain medical conditions like Alzheimers Disease. These models help diagnose diseases faster than traditional methods while reducing patient effort involved.

Problems solved by technology

The technical issue addressed in this patented method relates to improving accuracy when analyzing medical imagery during Alzheimers Disease (AD) diagnoctions due to variations between different individuals or ages that affect how well they look at certain parts of them. This requires highly skilled professionals with specialized knowledge about what kind of things happen inside each part of our brains.

Method used

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  • Alzheimer's disease detection device and computer equipment
  • Alzheimer's disease detection device and computer equipment
  • Alzheimer's disease detection device and computer equipment

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Embodiment Construction

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

[0020] Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0021] It s

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Abstract

The invention provides an Alzheimer's disease detection device and computer equipment, and relates to the field of medical disease detection. According to the application, a brain network constructionmodule constructs a to-be-detected brain network corresponding to a to-be-detected brain; a characteristic parameter calculation module calculates a network structure characteristic parameters of target frequent sub-graphs at the to-be-detected brain network corresponding to the target characteristic parameter type, wherein the target feature parameter types corresponding to different target frequent sub-graphs comprise any one or combinations of sub-graph local clustering coefficients, sub-graph importance scores and sub-graph local importance scores; a characteristic parameter classification module inputs the calculated network structure characteristic parameters into a brain detection model to perform Alzheimer's disease characteristic classification; and therefore, under the conditionthat the human brain structure characteristics with high matching degree corresponding to the brain to be detected are obtained, a high-precision Alzheimer's disease diagnosis result is obtained, andthe artificial participation degree in the diagnosis process is reduced.

Description

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Claims

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Application Information

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Owner SICHUAN UNIV
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