139 results about "Neural imaging" patented technology

The invention provides an Alzheimer disease auxiliary accurate identification method and system based on a multi-omics marker network and an adaptive support vector machine. Accurate identification of Alzheimer's disease and other types of dementia is realized through a marker network based on five modal data and an adaptive support vector machine model. According to the method, multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation are fused, specific markers are analyzed and screened by adopting a weighted gene co-expression network, and cross-modal feature interactive learning is realized through a self-attention mechanism. An online learning mechanism is introduced to enable the model to adapt to new data distribution, and the contribution degree of each marker to diagnosis is output in combination with an interpretability module. Finally, multi-center data synchronization and model optimization are realized by means of a cloud platform, the accuracy, specificity and early diagnosis capability of AD diagnosis and identification and the adaptability of the model are remarkably improved, the limitation of the prior art is overcome, and doctors are assisted to diagnose and treat the Alzheimer's disease.

The invention relates to an intelligent neural navigation system for neurosurgery and a use method thereof.The intelligent neural navigation system comprises an image collecting and processing unit, an intelligent planning unit, a positioning and tracking unit, a visual interaction unit and a data management and updating unit. The optimal operation scheme is selected through operation simulation operation, so that a doctor is helped to master the condition of a patient more clearly, personalized adjustment can be carried out in real time, the operation scheme better fits the actual condition of the patient and the operation habit of the doctor, and the success rate and the quality of an operation are improved. Meanwhile, an optical positioning sensor and an electromagnetic positioning sensor are integrated, accurate position information of the surgical instrument and the area, needing to be treated, of the patient is obtained in real time, operation of the surgical instrument is accurately guided, then it is ensured that the surgical instrument is accurately matched with the preoperative planned path, and mistaken damage to surrounding normal tissue in the surgical process is reduced; and the accuracy and the safety of the operation are improved.

The invention discloses a depression recurrence risk intervention method, device and equipment and a medium, and belongs to the technical field of medical treatment. The method comprises the following steps: integrating a gene risk score, neuroimaging brain region characteristics and clinical medical history data through a multi-modal fusion neural network, and generating an individual baseline risk score; the baseline score is dynamically corrected based on the self-assessment data, and the real-time performance of risk assessment is enhanced; analyzing time sequence characteristics of the physiological and behavior data by using an LSTM time sequence model, and outputting a short-term recurrence early warning label; and in combination with emotion knowledge graph analysis and dynamic risk grading of real-time voice / text data, a hierarchical intervention strategy is triggered. Through multi-dimensional data fusion and a dynamic calibration mechanism, the problems that a traditional method is single in evaluation dimension and lags in response are solved, closed-loop management from risk early warning to accurate intervention is achieved on the premise that direct clinical diagnosis is avoided, and comprehensiveness and timeliness of prevention and control of depression recurrence are improved.

The invention discloses an image genetics correlation analysis method based on comparative learning and a conditional generative network, and the method comprises the steps: firstly, carrying out the nonlinear mapping of multi-modal image data to a potential space, and extracting the features of the image data; then, implementing comparative learning among the multi-modal image features to enhance feature embedding; in order to adaptively learn the importance of feature embedding, an attention mechanism is introduced to effectively fuse multi-modal image data; in addition, single nucleotide polymorphism (SNP) data is preprocessed into numeric data through a sample population mapping matrix, and then the SNP data is further encoded using a variational autoencoder to extract potential genetic features; in order to construct a non-linear correlation between nerve image data and genetic data, SNP data and demographic data are utilized, a dual-identification condition generative adversarial network is proposed to generate a multi-modal nerve image and a modal attention vector, and early diagnosis of brain diseases can be realized.

The invention relates to the technical field of medical images, in particular to a neural CT map segmentation method based on high-precision registration. The method comprises the following steps: acquiring a nerve CT image; performing coarse registration based on mutual information according to the CT image, and performing multi-scale fusion based on wavelet to obtain a fused image; performing standard map preprocessing on a preset standard nerve CT map database according to the fused image to obtain a preprocessed standard map set; adding a multi-scale feature fusion module to a pre-established basic U-Net architecture, and embedding a space attention module to obtain a to-be-trained U-Net network; and performing deep registration network training on the to-be-trained U-Net network by using the fused image and the preprocessed standard atlas set to obtain a trained U-Net network. According to the invention, through technologies of multi-modal image fusion, deep learning registration and conditional random field optimization, a segmentation effect with higher precision and higher robustness is realized.

The invention discloses a multi-modal deep learning traceability method and system fusing magnetoencephalogram and electroencephalography, and relates to the technical field of artificial intelligence and neuroimage.Real magnetoencephalogram signals and electroencephalography signals are preprocessed and then input into a traceability model, the probability of occurrence of a source in a corresponding area is predicted, and the traceability of the source in the corresponding area is obtained by combining an imported source partition distance matrix. A final traceability result is obtained; the training process of the traceability model is as follows: constructing a generative adversarial network, and generating a multi-modal neural electrophysiological data set; inputting the multi-modal neural electrophysiological data set into a residual network of a double-branch structure, and performing stage hierarchical extraction and decoupling on magnetoencephalogram signals and electroencephalogram signals respectively; extracting features in different stages by using a multi-scale convolution module, fusing the extracted features, inputting the fused features into a classifier, defining a loss function, and updating trainable parameters of the traceability model; the traceability method improves the accuracy and generalization ability of traceability positioning.

The invention discloses a multi-mode autism diagnosis method and system based on resting state fMRI and phenotypic text information, a medium and a product. FMRI and phenotypic text information are processed through a pre-trained heterogeneous graph neural network model to output a diagnosis result; the resting state fMRI is utilized to construct a brain function connection graph, and the phenotypic text information is coded into a two-dimensional vector; respectively extracting brain connection feature embedding of the fMRI mode and phenotype information feature embedding of the text mode; fusing the feature embedding in the two modes through a gating fusion network; and constructing a heterogeneous group diagram based on the fusion features and phenotypic information similarity, and obtaining group diagram features on the heterogeneous group diagram by using dual-channel information aggregation and adaptive feature fusion to output a diagnosis result. The invention aims to improve the accuracy of autism diagnosis by using multi-modal information, and can be applied to the fields of neuroimaging analysis, intelligent medical treatment and the like.

The invention provides a method for analyzing Alzheimer's disease based on a multi-task graph convolutional network (GCN) of an attention mechanism, and aims to improve the accuracy of disease diagnosis and clearly identify a brain region related to the Alzheimer's disease. Most existing Alzheimer disease diagnosis models only depend on medical image data and ignore clinical score information related to diseases, so that brain areas affected by the diseases cannot be effectively recognized; optimization is achieved through the following steps that firstly, segmentation based on tissue types is conducted on brain areas, and initial weights are randomly distributed to each area; thirdly, a multi-task attention unit is introduced, and cross interaction between medical images and clinical scores is promoted by capturing shared feature information between brain areas and between tasks; finally, a specific task layer is designed for each task, and accurate prediction of the Alzheimer's disease and related brain areas is carried out. Experimental results show that the accuracy of the method is superior to that of an existing method on an Alzheimer's disease neuroimaging proposal (ADNI) data set, and the brain area related to the disease can be clearly recognized.

The invention provides a brain region grey matter layering method, device and equipment based on diffusion magnetic resonance imaging, and can be applied to the technical field of neural image calculation. The method comprises the following steps: acquiring diffusion magnetic resonance imaging data corresponding to a target brain area of a target object; processing the signal feature data by using an encoder to obtain encoding feature vectors corresponding to the N grey matter areas respectively; for the nth grey matter area of the N grey matter areas, clustering at least one voxel located on the nth grey matter area based on a preset clustering number k and the coding feature vector to obtain an nth clustering result; for the mth cluster of the k clusters, determining sorting information of the mth cluster according to the position information of voxels in the mth cluster, the position information of the first interface and the position information of the second interface; and sorting the k clustering clusters according to the sorting information of the k clustering clusters to obtain a brain region grey matter layering result for the target object.

The present invention provides a method for extracting a neuroimaging biomarker based on an interpretable ensemble three-dimensional convolutional neural network (3DCNN) to address limitations in the prior art. The present invention derives a novel neuroimaging biomarker P-score from prediction results obtained by an ensemble three-dimensional convolutional neural network model. The solution can help researchers to conduct studies on longitudinal trajectory changes of structural magnetic resonance imaging (sMRI) during the progression of Alzheimer's disease, and analyze an association of the longitudinal trajectory changes with neurodegenerative changes of Alzheimer's disease subjects. The extracted neuroimaging biomarker can provide a basis for predicting a sequence of intervention of brain regions in the neurodegenerative changes of Alzheimer's disease patients and upcoming clinical symptoms.

The invention relates to the technical field of image processing, and discloses a neural image data classification method and system based on machine learning, and a storage medium. The method comprises the steps of carrying out artifact removal processing on multi-modal neural image data through an N4 bias field correction algorithm to obtain a standardized data set, constructing a cross-modal feature fusion matrix to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a three-dimensional residual convolutional network for deep learning to obtain discriminative feature representation, carrying out classification through a hierarchical integrated classifier to obtain a result, and carrying out classification through a three-dimensional neural network. And performing incremental learning on the newly added data to obtain an updated label. The technical problems that in an existing neural image data analysis technology, multi-modal data cannot be effectively fused, single feature representation discrimination is insufficient, deep network training is difficult, and continuous learning ability is lacked are solved.

The invention discloses a brain connection event causal relationship extraction method based on weighted joint constraint learning. The method aims at a text mining task-brain connection event causal relationship extraction in an event-level neural image. According to the method, a novel model named as a brain connection event relation miner is developed by utilizing weighted joint constraint learning. A lightweight model is developed by integrating a BiLSTM (Bidirectional Long Short Term Memory) network and a multilayer perceptron (MLP), and is used for jointly extracting a plurality of event causal relationships from brain connection literatures. In view of scarcity of an annotation brain connection corpus, a weighted joint constraint learning framework is provided. The framework integrates dual consistency constraints, including common sense constraints and domain constraints, and combines the dual consistency constraints with adaptive weight learning to enhance the small sample learning ability of the model.

The present disclosure relates to the field of image processing, and in particular to a lesion identification method and apparatus for a neural image, and an electronic device and a storage medium. The method comprises: determining a neural image set to be identified comprising a first structure image, a first lesion image and a first metabolic image; performing image pre-processing on the neural image set to obtain an object image set comprising a second structure image, a second lesion image and a second metabolic image; inputting the object image set into a trained three-dimensional convolutional neural network to obtain a lesion location of a target object; and performing lesion location labeling on the first structure image on the basis of the lesion location of the target object, so as to obtain and display a lesion location image. In the present disclosure, by pre-processing a multi-modal neural image and performing lesion detection on the multi-modal neural image by means of a neural network, a lesion location in the neural image can be accurately determined. In addition, in the present disclosure, an original neural image is directly labeled on the basis of an identification result, so that a user can visually view the lesion location.

The invention belongs to the technical field of intelligent diagnosis of mental diseases, and particularly relates to a major depression diagnosis system. The system comprises an input module used for inputting indexes of a patient; wherein the indexes comprise at least one of the following information: a neurocognitive test, child trauma information, multi-modal neuroiconomics information and transcriptomics information; the prediction module is integrated with a machine learning model and is used for obtaining a prediction result about whether the patient suffers from the major depression or not according to the indexes; and the output module is used for outputting the prediction result. The system provided by the invention can accurately diagnose the major depression, can effectively assist doctors in diagnosing and screening the major depression, reduces the workload of workers, and has a very good application prospect.

The embodiment of the invention discloses an autism spectrum disorder subtype division method and device, a medium and a program product. The method comprises the following steps: constructing a connection brain map based on neuroimaging data and a functional brain region division template of an ASD individual; constructing a brain age regression model, predicting the social brain age of the ASD individual based on the connection brain map and the brain age regression model, and obtaining the brain age difference of the ASD individual in combination with the actual brain age of the ASD individual; obtaining an ADOS social score of the ASD individual, and carrying out clustering analysis on the ADOS social score and the brain age difference to obtain a clustering subtype; and carrying out behavioral verification and neural dimension verification on the clustering subtypes, and constructing a combined portrait among the subtypes, the behavior features and the neural features based on a verification result. According to the method, the social brain age can be predicted by constructing the connection brain map, the clustering subtypes are divided, the combined portrait is constructed, a doctor can be accurately assisted to detect ASD subtype neural development differences, and discovery and application of subtype specific biomarkers are assisted.

The invention provides a schizophrenia core epicenter region identification method based on a multi-mode nerve image, and belongs to the technical field of medical image analysis and neuropsychiatric disease diagnosis. According to the method, a macroscopic-mesoscopic-microscopic covariant network model is established by integrating multi-modal nerve image data (including structural magnetic resonance, functional magnetic resonance imaging and diffusion tensor imaging) and mesoscopic-microscopic scale data (gene expression atlas, neurotransmitter distribution and cell construction characteristics). The method comprises the following steps: firstly, constructing various connection networks, then identifying a core epicenter region with abnormal grey matter thickness based on a graph theory algorithm, carrying out cross-scale matching on the epicenter region and gene expression, neurotransmitter and cell structure characteristics by utilizing spatial correlation analysis, and analyzing a diffusion path of the epicenter region through connection omics. By fusing the multi-scale biomarkers, the limitation of single modal analysis is broken through, and systematic analysis of the schizophrenia pathological network is realized.

The invention belongs to the technical field of medical image processing and neural image analysis, and particularly discloses a cerebral apoplexy prognosis prediction method which comprises the following steps: step 1, performing whole cerebral vessel segmentation and morphological feature extraction on a CTA image to obtain 40 blood vessel morphological features; 2, extracting high-throughput omics characteristics from an infarction core and a half-dark band region in the DWI image, and obtaining 1026 image omics characteristics; 3, screening high-resolution features based on minimum absolute contraction and a selection operator, establishing a support vector machine classification model, and predicting a prognosis classification result of a three-month improved Rankin scale of the stroke patient; the deep learning network is used for automatically segmenting the whole brain blood vessel, so that subjective difference of manual recognition is reduced; starting from pathophysiology of occurrence and development of cerebral apoplexy, vascular morphological characteristics and infarction region imageomics characteristics are comprehensively incorporated to carry out prognosis prediction on cerebral apoplexy.

The invention provides an EEG function connection prediction method based on fMRI deep cross-modal representation learning, and relates to the crossing field of neuroiconography and artificial intelligence. According to the method, a sample pair is constructed through time alignment of EEG and a blood oxygen level dependent signal, an end-to-end deep neural network architecture composed of a time projection unit, a cross-attention fusion encoder and a connection synthesis head is designed, and direct mapping from a BOLD signal to EEG function connection is achieved. The model adopts a composite loss function, and gives consideration to the consistency of the prediction connection matrix with the real EEG function connection in numerical precision and topological mode, thereby achieving the high-fidelity reconstruction of the EEG function connection map at the frequency domain level. According to the invention, the topological structure stability of the brain function network can be effectively maintained. Under the conditions of incomplete EEG data, serious noise interference or complete loss, the frequency domain function connection characteristics can still be stably recovered, and a reliable analysis substitution path is provided for brain function network research.

The invention relates to the technical field of medical image processing, in particular to a multi-time-point neural image comparative analysis method and system.The method comprises the following steps that a first three-dimensional neural image matrix and a second three-dimensional neural image matrix are obtained, rigid transformation matrix registration is calculated, and a third three-dimensional neural image matrix is generated; constructing a three-dimensional elastic grid model and adjusting node coordinates based on a mutual information criterion; generating a three-dimensional displacement vector field; constructing a Jacobian matrix and calculating determinant values; executing natural logarithmic transformation to generate a microscopic volume change rate map; and calculating a logarithm Jacobian mean value and determining a volume change state. According to the method, pose deviation is eliminated by calculating a rigid transformation matrix, a vector field reflecting fine displacement is constructed by utilizing a mutual information criterion, voxel-level quantitative analysis is realized in combination with a Jacobian matrix and logarithmic transformation, and minimal lesion recognition precision and diagnosis efficiency are improved in cooperation with a standard brain anatomy template.

The invention provides a neural cognitive function evaluation method and device based on artificial intelligence, and relates to the technical field of neural cognitive function evaluation. The method comprises the following steps: collecting neuroimaging data, physiological signal data and behavior data; the collected multiple kinds of neurological function cognition evaluation data are preprocessed; performing feature extraction on the preprocessed data to obtain corresponding feature data; constructing a convolutional neural network evaluation model based on a capsule network combination graph, training the model by using a training set, and optimizing model hyper-parameters by using an ant lion algorithm; and evaluating the model through the evaluation index. According to the method, various data related to the neurocognitive function are collected, the convolutional neural network evaluation model based on the capsule network and the graph is constructed, and the model hyper-parameters are optimized through the ant lion algorithm, so that a hidden mode in the neuroimage data can be better mined, comprehensive evaluation on the cognitive function from multiple aspects is realized, and the evaluation efficiency is improved. And the accuracy and applicability of neurocognitive function evaluation are improved.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.

The present invention relates to the field of medical imaging technology, and in particular to a neural CT atlas segmentation method based on high-precision registration. The method comprises the following steps: obtaining a neural CT image; performing a coarse registration based on mutual information according to the CT image, and performing a multi-scale fusion based on wavelet to obtain a fused image; performing standard atlas preprocessing on a preset standard neural CT atlas library according to the fused image to obtain a preprocessed standard atlas set; adding a multi-scale feature fusion module to the pre-built basic U-Net architecture, and embedding a spatial attention module to obtain a U-Net network to be trained; performing deep registration network training on the U-Net network to be trained using the fused image and the preprocessed standard atlas set to obtain a trained U-Net network. The present invention achieves a higher precision and more robust segmentation effect through the techniques of multimodal image fusion, deep learning registration, and conditional random field optimization.

The invention belongs to the technical field of small animal mental disease diagnosis, and provides a neuroimaging-based small animal mental disease diagnosis system, which comprises an image data acquisition and preprocessing module for performing multi-modal imaging on a small animal by using high-precision neuroimaging equipment, acquiring neuroimaging data, preprocessing the acquired image data, and acquiring neuroimaging data; comprising the steps of denoising, registration, segmentation and the like so as to improve data quality; a feature extraction and analysis module; according to the invention, the resolution and the signal-to-noise ratio of the image data are improved through the multi-modal neural imaging technology, and a reliable data basis is provided for accurate diagnosis; image features are automatically extracted and analyzed through a deep learning algorithm, human intervention is reduced, and the accuracy and efficiency of diagnosis are improved; a personalized diagnosis model is constructed, individual differences of small animals are considered, and more accurate diagnosis is realized; through introduction of the cloud data management platform, centralized storage, sharing and cooperative analysis of data are realized.

The invention relates to a method for determining an individualized nerve regulation target spot, and the method comprises the steps: obtaining nerve image data, which are repeatedly measured for multiple times, of a subject and corresponding clinical psychological assessment data in a preset time period; calculating a brain activity index of each voxel in a preset brain region; establishing a plurality of candidate mathematical models to describe the relationship between the brain activity indexes and the clinical scores; selecting an optimal model through statistical test of fitting residual errors; generating a parameter weight map representing'symptom-activity 'association strength based on the optimal model; and finally, determining an individualized nerve regulation target according to the numerical distribution of the map. The invention also relates to a corresponding device, a processor and a computer readable storage medium thereof, and solves the problem of inaccurate target positioning caused by neglecting symptom heterogeneity and individual brain function difference in the prior art through a core methodology of ''longitudinal tracking-multi-model fitting-residual optimal selection''. The curative effect of nerve regulation and control treatment is obviously improved.

The invention relates to the technical field of medical health, and provides an AD early warning model construction method and system based on a multi-dimensional test, and the method comprises the steps: collecting nerve image data, biomarker data and clinical feature data of a plurality of subjects, carrying out the abnormal data screening of a nerve image data set, carrying out the data cleaning of a biomarker data set, and carrying out the data cleaning of the biomarker data set; carrying out data coding on the clinical feature data set, respectively carrying out feature extraction on the preprocessed neural image data set and the preprocessed biomarker data set to obtain a neural image feature set and a biomarker feature set, and constructing a multi-dimensional feature matrix set; and obtaining a weighted multi-dimensional feature matrix set by using the training set matrix subset and the test set matrix subset, training the clinical prediction model by using the weighted multi-dimensional feature matrix set to obtain an early warning experiment model, and testing the early warning experiment model to obtain an early warning model. The problems of low diagnosis precision and abnormal data interference of a single data source can be solved.

The invention provides a neural image data preprocessing system and a neural image data preprocessing method, and the neural image data preprocessing system comprises a file input module, a workflow module, a workflow scheduling module and a file output module. The file input module is used for carrying out format recognition and conversion on original nerve image data to obtain nerve image data in a standard format; the workflow module is used for generating a corresponding to-be-processed task according to the data information of the neural image data in the standard format; the workflow scheduling module is used for distributing computational nodes for tasks to be processed and completing preprocessing of the neural image data in the standard format; and the file output module is used for performing standardized naming and storage on the preprocessing result corresponding to the original nerve image data. The invention provides a neural image data preprocessing system and a neural image data preprocessing method. The processing efficiency of neural image data is improved.

The invention discloses a neural signal extraction self-supervised learning method and system based on sparse decomposition, belongs to the technical field of deep learning computer image processing, and solves the problems that in an existing traditional neural signal extraction method, calculation time is long, and in an existing deep learning neural signal extraction method, calculation time is short. Signals need to be labeled manually; and the extraction accuracy is low. Comprising the following steps: obtaining a denoised neural microscopic image as a data set; constructing a sparse decomposition network, and training the sparse decomposition network by adopting a loss function; inputting the denoised neural microscopic image into the trained sparse decomposition network for reasoning to obtain a sparse part image representing neuron information; drawing an F / F image, performing threshold segmentation processing, and extracting spatial positions of neurons; according to the spatial positions of the neurons, F / F time sequence signals of each neuron are extracted, and the spatial position and starting and ending time of each neuron event are recorded; the method is suitable for a neural imaging technical scene.

The invention provides a neural image analysis method and system based on deep learning. The method comprises the following steps: receiving neural image original data; carrying out multi-modal feature collaborative extraction on the original data of the neural image to generate an initial feature map; inputting the initial characteristic spectrum into a deep characteristic fusion network to carry out enhanced recognition on the lesion area to obtain a lesion area characteristic spectrum; and performing three-dimensional topological structure analysis on the lesion area characteristic spectrum, and constructing a lesion area three-dimensional structure model. According to the method, the spatial form, the volume and the surface topological relation of the lesion area can be accurately represented, a concrete structural basis is provided for surgical path planning, and the reliability of surgical path planning is improved.

The application belongs to the technical field of biomedical engineering and brain science, and particularly relates to a heterogeneous whole brain model construction method based on linearization theory and fusing connection-nonconnection effects, which constructs a heterogeneous whole brain model, uses real structural connections obtained through fiber bundle tracking as model input, compresses heterogeneous parameters based on real neural image data, introduces the influence of nonconnection effects on the whole brain model after completing parameter optimization, and solves the nonconnection effects through mathematical simplification, thereby providing a specific method for constructing individual-level heterogeneous whole brain models. The application introduces real neural image data, overcomes the difficulty of high-dimensional parameter optimization, and enhances the biological rationality of heterogeneous parameters, and simultaneously significantly improves the speed and stability of parameter optimization through the linearization method.

The invention relates to the technical field of neural images, and discloses a brain health level evaluation system and method based on neural image.The brain health level evaluation system comprises an imaging unit, a preprocessing unit, a segmentation unit, an analysis unit, a center unit, a treatment unit and an evaluation unit, the preprocessing unit preprocesses the neural image, the segmentation unit segments the preprocessed neural image, the central unit receives an area difference value and a graying deviation value and calculates a form value XT and an activity value HD, and the evaluation unit calculates an evaluation value P according to the form value XT and the activity value HD. According to the method, the generated nerve image is processed, the accuracy of the nerve image is ensured, the evaluation value P calculated by adopting the form value XT and the activity value H can accurately reflect the self condition of the examiner, and the smaller the evaluation value P is, the higher the brain health level is.