Automatic distinguishing system for autism spectrum disorder, storage medium and equipment

A technique for autism and pedigree, which is applied in medical automated diagnosis, proteomics, character and pattern recognition, etc., can solve the problems of low accuracy rate, inability to provide reliable auxiliary diagnosis for early ASD diagnosis, single analysis features, etc., to achieve Improve the accuracy of discrimination, good learning effect, and improve the effect of classification accuracy

Active Publication Date: 2021-11-30
SHANDONG JIANZHU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The inventors found that the existing diagnosis of autism spectrum disorder has the problem of single analysis features and low accuracy, which cannot provide reliable auxiliary diagnosis for early ASD diagnosis

Method used

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  • Automatic distinguishing system for autism spectrum disorder, storage medium and equipment
  • Automatic distinguishing system for autism spectrum disorder, storage medium and equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] refer to figure 1 , a kind of automatic discriminating system of autism spectrum disorder of the present embodiment, it comprises:

[0043] (1) Brain connection feature extraction module, which is used to extract functional connection and white matter connection map from the multimodal magnetic resonance images of the subjects as brain connection features.

[0044] Among them, the subjects were from patients with autism spectrum disorders and normal controls from the ABIDE Phase I / II database, and their sMRI images, fMRI images and dMRI images were obtained respectively.

[0045] In one or more embodiments, the system for automatic discrimination of autism spectrum disorder further includes an image preprocessing module, which is used for preprocessing the multimodal magnetic resonance images of the subjects.

[0046] The preprocessing process of each modal feature is as follows:

[0047] For sMRI, ①manually adjust the origin of all sMRIs to the anterior joint point; ...

Embodiment 2

[0071] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented:

[0072] Extract functional connectivity and white matter connectivity maps from the multimodal magnetic resonance images of the subjects as brain connectivity features;

[0073] Sparse selection and nonlinear dimensionality reduction of brain connection features;

[0074] Based on the whole genome sequencing data of the subjects and the quality screening of single nucleotide polymorphisms, the genetic data features are extracted by using the genetic constraint maximum likelihood method;

[0075] Use the canonical correlation analysis method to fuse the brain connection features and genetic data features after selection and dimensionality reduction;

[0076] Input the fused features into the decision-making model to determine whether it belongs to the category of autism spectrum disorde...

Embodiment 3

[0080] This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, the following steps are implemented:

[0081] Extract functional connectivity and white matter connectivity maps from the multimodal magnetic resonance images of the subjects as brain connectivity features;

[0082] Sparse selection and nonlinear dimensionality reduction of brain connection features;

[0083] Based on the whole genome sequencing data of the subjects and the quality screening of single nucleotide polymorphisms, the genetic data features are extracted by using the genetic constraint maximum likelihood method;

[0084] Use the canonical correlation analysis method to fuse the brain connection features and genetic data features after selection and dimensionality reduction;

[0085] Input the fused features into the decision-making model to determine whether it belong...

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Abstract

The invention belongs to the field of computer-aided diagnosis based on medical images and genetics, and provides an automatic distinguishing system for autism spectrum disorders, a storage medium and equipment. The system comprises a brain connection feature extraction module which is used for extracting functional connection and white matter connection maps from a tested multi-mode magnetic resonance image as brain connection features; a feature selection and dimension reduction module which is used for performing sparse selection and nonlinear dimension reduction on brain connection features; a genetic data feature extraction module which is used for extracting genetic data features by utilizing a gene constraint maximum likelihood method based on tested whole genome sequencing data and single nucleotide polymorphism quality screening; a feature fusion module which is used for fusing the selected and dimensionality-reduced brain connection features and genetic data features by using a canonical correlation analysis method; and a classification decision-making module which is used for inputting the fused features into a decision-making model and judging whether the features belong to the autism spectrum disorder category or not.

Description

technical field [0001] The invention belongs to the field of computer-aided diagnosis based on medical imaging and genetics, and in particular relates to an automatic discrimination system, storage medium and equipment for autism spectrum disorders. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Autism Spectrum Disorder (ASD) is a comprehensive mental developmental disorder with onset in infancy, mainly manifested as abnormal interpersonal communication and communication patterns, language and non-verbal communication obstacles, limited interests and activities, and behavioral disorders. Rigid, repetitive. Finding an accurate and objective early ASD discrimination method is of great significance for assisting the intelligent diagnosis of clinical ASD and formulating reasonable early neuroprotective measures. [0004] Some studies have i...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16B20/40G06T7/136G06T7/11G06T7/00G06K9/62G06K9/00
CPCG16H50/20G16B20/40G06T7/0012G06T7/11G06T7/136G06T2207/10088G06T2207/20081G06T2207/30016G06F2218/08G06F18/2411
Inventor 魏珑袭肖明宁阳郝凡昌王纪奎
Owner SHANDONG JIANZHU UNIV
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