16 results about "Diagnosis Classification" patented technology

The invention relates to an autism spectrum disorder screening application system which comprises a front-end user interaction device and a background data processing server. The front-end user interaction device comprises a child information management module; the scale screening module is used for providing at least one autism screening scale for the user to select and fill, and generating a scale screening score after receiving a scale answer filled by the user; the photo screening module is used for receiving at least one child front face image uploaded by the user, preprocessing the face image and then calling a photo analysis model to generate a photo screening score; the screening process of the front-end user interaction device comprises the steps that after a user selects a target child through the child information management module, operation of the scale screening module and the photo screening module is completed in sequence, and the background data processing server generates a comprehensive score and a risk level for scale screening scores and photo screening scores through a weighted voting mechanism. Through the application system, accurate ASD pre-diagnosis classification can be quickly provided.

The invention relates to the technical field of intelligent diagnosis of medical images, and discloses an intelligent diagnosis and classification method for biopsy pathological images. The method comprises the following steps: acquiring digital pathological image data of a biopsy sample, and extracting a morphological feature region in the image as a basic analysis unit; and constructing a multi-scale feature fusion network, inputting the basic analysis unit into the first feature extraction layer to obtain a primary feature map, and performing spatial attention weighting through the secondary feature extraction layer to generate a fusion feature vector. And establishing a dynamic classification threshold pool, dividing feature subspaces according to the dimension distribution of the fusion feature vector, and distributing independent classification boundary parameters for each subspace. And loading a pre-trained pathological knowledge map, carrying out similarity matching on the fusion feature vector and node features in the map, and screening out knowledge nodes of which the matching degrees exceed a preset threshold value as an auxiliary diagnosis basis. And generating an initial classification label based on an output result of the dynamic classification threshold pool and an auxiliary diagnosis basis, and outputting a final diagnosis classification result.

The invention belongs to the technical field of biomedical engineering, and discloses a multiple biomarker detection system for diagnosis and classification of hepatic failure acute kidney injury, comprising a biomarker library construction module for obtaining historical hepatic failure acute kidney injury data to construct a biomarker library; the historical hepatic failure acute kidney injury data comprises biomarker data, pathological image data and patient clinical data; the data processing module is used for preprocessing the acquired biomarker data, pathological image data and patient clinical data to obtain a biomarker feature data set, a pathological image feature data set and a clinical feature data set; the acute kidney injury diagnosis module is used for fusing the biomarker feature data set, the pathological image feature data set and the clinical feature data set to obtain a comprehensive feature data set; inputting the comprehensive feature data set into a trained acute kidney injury diagnosis model, and predicting to obtain acute kidney injury type data; and the diagnosis accuracy is improved.

The present application relates to the technical field of medical image intelligent diagnosis, and discloses a biopsy pathological image intelligent diagnosis classification method. The method collects digital pathological image data of biopsy samples, extracts morphological feature regions in the image as a basic analysis unit. A multi-scale feature fusion network is constructed, the basic analysis unit is input into a first feature extraction layer to obtain a primary feature atlas, spatial attention weighting is performed through a secondary feature extraction layer, and a fusion feature vector is generated. A dynamic classification threshold pool is established, a feature subspace is divided according to the dimension distribution of the fusion feature vector, and independent classification boundary parameters are allocated to each subspace. A pre-trained pathological knowledge graph is loaded, the fusion feature vector is matched with the node features in the graph in terms of similarity, and knowledge nodes with a matching degree exceeding a preset threshold are selected as auxiliary diagnosis basis. An initial classification label is generated based on the output result of the dynamic classification threshold pool and the auxiliary diagnosis basis, and a final diagnosis classification result is output.

The invention relates to a multimodal data fusion-based pigmented disease diagnosis method, apparatus and device, and a medium. The method comprises the following steps: firstly, acquiring multi-modal data of pigmented diseases, and unifying resolution and wavelength range to obtain a standardized multi-modal data set; on the basis of the data set, each modal feature is extracted by using a convolutional neural network, a data quality score is calculated, a replacement weight is calculated for a missing modal, and a multi-modal feature data set is obtained through complete modal correlation feature supplementation. Setting a weight according to a lesion feature diagnosis contribution degree, fusing features such as asymmetry and diameter change by using weighted average to generate a fusion feature vector, and performing principal component analysis dimensionality reduction to obtain a dimensionality reduction feature vector; finally, diagnosis classification is judged according to a preset malignant correlation threshold value, similar risk cases are grouped in combination with a clustering algorithm, and a personalized diagnosis and treatment scheme is generated according to risk characteristics and diagnosis and treatment requirements. According to the method, the accuracy and reliability of pigmented disease diagnosis can be improved, risk grouping and personalized diagnosis and treatment are realized, and the treatment pertinence is improved.

The invention discloses a computer-aided diagnosis method and system for Alzheimer's disease analysis, and relates to the technical field of medical information processing, and the key points of the technical scheme are as follows: the method comprises the following steps: obtaining a T1 weighted brain magnetic resonance image of a subject; dividing the two-side sea horse tooth-shaped gyrus areas; image omics features are extracted from the subregions; selecting a group of discriminative feature combinations containing texture features from the right dentate loop and first-order statistical features from the left dentate loop through a multi-layer screening strategy; and inputting the feature combination into a pre-trained machine learning model to generate a diagnosis classification result. According to the method, high-precision and high-sensitivity diagnosis of the Alzheimer's disease is realized by using non-invasive standard MRI data and capturing the microstructure change of dentate gyrus, and the diagnosis result has strong correlation with key plasma biomarkers and cognitive level, so that the method has important clinical application value.

The invention discloses an occupational pneumoconiosis screening and management system based on multi-source data fusion, and relates to the technical field of occupational disease prevention and treatment, the system comprises a multi-source data acquisition module, a data fusion module, a screening diagnosis module, a scheme making module and a follow-up visit management module; image data, physiological data, basic clinical data, biomarker detection data and treatment process data are integrated through the multi-source data acquisition module, the multi-source data are classified, cleaned and standardized through the data fusion module, features are extracted, and a multi-dimensional data model is constructed. A classification diagnosis model is built based on multi-center prospective queue research data, health state and illness state classification of the examinee is completed, so that limitation of a single data dimension is broken, complementarity of multi-source data is utilized to provide a comprehensive basis for diagnosis classification, classification standards are more scientific and universal through queue research data support, and classification accuracy is improved. And a solid foundation is laid for formulating an intervention scheme.

The present application relates to the detection and diagnosis of malignant biliary strictures, in order to solve the problem that the prior art cannot accurately locate and sample in real time during DSOC operation, the present application provides a real-time detection and diagnosis system and method based on malignant biliary strictures during DSOC operation, including data acquisition and quality control module for collecting video data during DSOC operation, output quality control frame sequence; The first stage morphological feature detection module is used for detecting and positioning a plurality of malignant related morphological features, and outputting the boundary box coordinates and the confidence; The second stage benign and malignant diagnosis classification module is used for carrying out benign and malignant two classification on the quality control frame sequence, and outputs the malignant probability and the two classification results; The explainability module is used for generating a heat map and spatial consistency analysis, and obtaining a visual video frame sequence; The deployment demonstration module is used for real-time receiving, displaying and storing the intraoperative DSOC video data, the boundary box coordinates and the confidence, the video level diagnosis result and the visual video frame sequence.

The invention discloses a depression diagnosis and analysis method based on a graph attention network, and relates to the technical field of medical signal processing, and the method comprises the steps: firstly, obtaining resting state nuclear magnetic resonance data, carrying out the preprocessing, and eliminating the interference of confounding factors through a nonlinear Gaussian random process; secondly, constructing a functional connection brain network and a diagnosis classification tag; thirdly, inputting the functional connection brain network and the diagnosis classification label into a depression classifier based on a graph attention network for feature learning and classification diagnosis; and finally, a graph attention mechanism is adopted, a brain region contribution degree scoring mechanism is established, topological characteristics of the high-identification brain region are obtained, and correlation information of the topological characteristics and clinical information is calculated. The combination is beneficial to revealing internal relations between brain structure and function changes and clinical symptoms, formulating personalized treatment schemes and improving the pertinence and effectiveness of treatment.

The application relates to a bearing fault diagnosis classification method based on multi-scale features and attention, which comprises the following steps: S1, collecting time domain signal data of a bearing in real time through an acceleration sensor; S2, extracting, analyzing and processing the pretreated data through a pre-constructed multi-scale feature classification module to obtain first data; S3, inputting the first data into a Transformer attention mechanism learning module for learning to obtain second data; and S4, inputting the second data into a full connection layer to output a diagnosis classification result of bearing faults; the application uses a convolutional neural network structure to construct a deep multi-scale feature extraction module, adopts a strategy of different size convolution kernels to mine shallow fault feature information, then introduces a pure attention mechanism to deeply filter fault features, retains the most representative features of the same fault in different working conditions, and can complete the classification of bearing faults in different working conditions.

The embodiment of the invention discloses a tinnitus diagnosis and classification system, and the system comprises the steps: carrying out the preprocessing of tinnitus nuclear magnetic resonance data to be classified through a preprocessing module, and obtaining the preprocessed nuclear magnetic resonance data; the construction module constructs N first connection matrixes under a preset brain atlas through the preprocessed nuclear magnetic resonance data; a node feature extraction module extracts node features of first nodes in the first connection matrixes to obtain first node feature vectors corresponding to the first nodes; the node feature optimization module performs node feature screening optimization on the node feature vectors of the first nodes to obtain second node feature vectors corresponding to important nodes, and the important nodes are key nodes in the first nodes; the fusion module fuses all the second node feature vectors to obtain a fused multi-atlas feature vector; and the classification module classifies the multi-map feature vectors to obtain tinnitus classification corresponding to the tinnitus nuclear magnetic resonance data. The technical problem that a patient cannot obtain targeted effective treatment due to the fact that the existing clinical detection rate of objective tinnitus is low is solved.

The invention relates to the field of medical image processing, and provides a brain disease diagnosis classification method and system based on a heterogeneity perception spectrogram convolutional network. The method comprises the following steps: based on a resting state brain function image of a subject, extracting brain function connections, calculating Pearson correlation between the brain function connections, and obtaining node features of a crowd graph; according to the relation between different subjects, a phenotypic encoder is adopted to construct the weight of edges between nodes so as to construct a crowd graph; based on the crowd graph, calculating local similarity between the nodes and neighbor nodes thereof to obtain local similarity weights of all the nodes; based on the local similarity weights of all the nodes, using an MLP layer to obtain weights of different layers for guiding feature fusion among the layers to obtain node feature representations of all the layers; splicing the feature representations of all layers by adopting simplified spectrogram convolution to obtain multi-layer feature representations; and based on multilayer feature representation, obtaining a brain disease classification probability matrix, and obtaining a classification result.

The application provides an auxiliary diagnosis method, device, equipment and medium for attention deficit hyperactivity disorder, which comprises the following steps: obtaining behavior data of a subject completing a plurality of executive function tasks; generating a binary evaluation result for each executive function based on the behavior data; wherein the binary evaluation result is obtained by selecting a core index from a plurality of evaluation indexes corresponding to each executive function, and comparing the numerical value of the core index with a corresponding preset threshold; inputting the binary evaluation result as an input feature into an auxiliary diagnosis classification model based on a decision tree, and outputting auxiliary diagnosis data; the application provides effective input data for the auxiliary diagnosis classification model based on the decision tree through binary evaluation, facilitates model calculation, adopts a diagnosis model based on a decision tree, simulates a thinking process of clinical first item evaluation and then comprehensive diagnosis, makes the diagnosis result easy to be understood and verified by doctors, and greatly improves clinical practicability and acceptance.

The application relates to the technical field of medical big data processing and artificial intelligence auxiliary diagnosis, in particular to an early Alzheimer's disease diagnosis method, which comprises the following steps: preprocessing and registering structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) image data of a to-be-diagnosed object, constructing a mixed feature pyramid to extract multi-scale anatomical features, adopting a space-time manifold embedding module to extract dynamic functional features, strengthening pathological correlation features through a cross-dimension double attention mechanism, and finally realizing diagnosis classification through multi-modal feature adaptive fusion. The application can accurately capture the deep correlation between brain structure microlesions and functional network abnormalities, and significantly improve the diagnosis accuracy of Alzheimer's disease and early mild cognitive impairment.

The present invention relates to a depression diagnosis system using multiparadigm brainwaves and machine learning. According to one embodiment of the present invention, a depression diagnosis system using multiparadigm brainwaves and machine learning may include: a brainwave measurement unit that measures a plurality of paradigm brainwaves from a subject; a brainwave extraction unit that extracts and preprocesses necessary multiparadigm brainwaves from the measured plurality of paradigm brainwaves to minimize noise; a model generation unit that creates a diagnosis model by performing machine learning using the extracted multiparadigm brainwaves; and a diagnosis unit that diagnoses the presence of depression using the generated diagnosis model. As such, according to the present invention, by generating a diagnostic model of a subject using multiparadigm brainwaves and diagnosing the presence of depression, the limitations of conventional diagnostic classification using a single paradigm can be overcome. Furthermore, by generating and applying a model through machine learning using multiple key brainwave biomarker data, the classification performance of depression with psychopathological diversity can be enhanced.

The invention discloses a traditional Chinese medicine intelligent tongue diagnosis method based on a causal relationship, a medium and equipment, and belongs to the technical field of intelligent medical treatment. The method comprises the steps that a causal generation type tongue diagnosis model is constructed, the causal structure of the model comprises a potential physiological state, a tongue picture feature and a tongue picture observation and diagnosis result four-element group, and a one-way causal direction from the potential physiological state to the tongue picture feature and then to the tongue picture observation and diagnosis result is preset; constructing a causal inference encoder, and inferring a first potential physiological state from the original tongue picture observation data along a causal reverse direction; constructing a mechanical decoder, and generating tongue picture characteristics and tongue picture observation data from the first potential physiological state along a causal positive direction in combination with the environmental noise; and constructing a diagnosis classifier, and generating a syndrome classification result according to the first potential physiological state. According to the method, through a cause and effect mechanism of explicit modeling tongue picture generation, model learning is guided to accord with real association of traditional Chinese medicine pathology, and reliability, robustness and interpretability of tongue diagnosis analysis can be effectively improved.