Alzheimer's disease detection method based on data space transformation

A data space and data technology, applied in diagnostic recording/measurement, medical simulation, medical automated diagnosis, etc., can solve the problems of increasing available training samples for disease diagnosis, low accuracy of auxiliary diagnostic models, inconsistent distribution of different data sets, etc., to achieve Extensive application of scalability, improved accuracy and generalization capabilities, and expanded training data sets

Active Publication Date: 2019-12-03
深圳龙岗智能视听研究院
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Problems solved by technology

[0003] The purpose of the present invention is to provide a detection method for Alzheimer's disease based on data space transformation, which solves the problem of inconsistent distribution of different data sets through the data spac...

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  • Alzheimer's disease detection method based on data space transformation
  • Alzheimer's disease detection method based on data space transformation
  • Alzheimer's disease detection method based on data space transformation

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[0024] The invention provides a detection method for Alzheimer's disease based on data space transformation, which adopts machine learning and data space transformation algorithm, and is a brand-new detection method for Alzheimer's disease. This method is an fMRI-based AD auxiliary diagnosis method suitable for small sample sets. It uses the data space transformation method to solve the problem that an accurate and robust machine learning model cannot be established due to insufficient sample size; at the same time, it uses machine learning algorithms to establish an auxiliary diagnosis. The model achieves accurate and robust AD disease detection, and its detection accuracy is greatly improved compared with traditional machine learning-assisted diagnosis methods.

[0025] The principle of the present invention is: 1) calculate the correlation coefficient between any two time series in the region of interest time series of each scanning, this correlation is undirected, obtains t...

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Abstract

The invention discloses an Alzheimer's disease detection method based on data space transformation, and the method comprises the following steps: 1, modeling brain functional magnetic resonance imaging (fMRI) data of all data sets, and extracting features from a data model; 2, performing feature selection on the features extracted in the previous step; 3, mapping the selected feature data of all the data sets into the same subspace by using a data space transformation method; 4, training a machine learning classification model by using the data cross validation in the subspace, and adjusting parameters to obtain an optimal computer-aided diagnosis (CAD) model. According to the method, the problem of inconsistent distribution of different data sets is solved, available training samples fordisease diagnosis are increased, and the conditions of low accuracy and insufficient generalization ability of an auxiliary diagnosis model due to insufficient sample size are relieved; meanwhile, theAD auxiliary diagnosis accuracy based on the fMRI data is greatly improved by using feature engineering and a machine learning algorithm.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis, in particular to a method for detecting Alzheimer's disease based on data space transformation. Background technique [0002] Computer-aided diagnosis (CAD) has been a hot topic in Alzheimer's disease (AD) research. At present, AD diagnosis mainly relies on the clinical manifestations of patients. In the early stage of the disease, patients may have no obvious cognitive impairment or only It manifests as mild memory impairment, which makes it difficult to distinguish patients from normal aging. Due to the lack of more specific indicators in the diagnosis of the disease, the early diagnosis rate is very low. Accurate and early diagnosis of the disease is very important for timely treatment and reducing the risk of patients, so early and accurate judgment of the disease degree using CAD system is very important for the prognosis and treatment of patients. Functional Magnetic Reso...

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Application Information

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IPC IPC(8): G16H50/20G16H50/50A61B5/00
CPCG16H50/20G16H50/50A61B5/4088A61B5/72
Inventor 赵翼飞李楠楠张世雄李若尘李革安欣赏张伟民
Owner 深圳龙岗智能视听研究院
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