34 results about "Diagnosis tool" patented technology

Methods and systems are provided for information retrieval pathway guidance. In particular, the systems and methods provided apply artificial intelligence to novel tasks related to retrieving remotely accessible data such as detecting competency levels of users, generating personalized guidance for individual users based on the retrieval goals and initial competency level of a user, generating custom diagnostic assets for those goals based on current strengths and weaknesses, generating content for custom questions for those diagnostic assets, and dynamically tracking and updating the competency level of the user during the course.

The invention provides a breast cancer patient pCR prediction classification method based on image-gene interpretability deep learning. The method comprises the following steps: dividing an obtained data set into a training set and a test set; splitting all the three-dimensional MRI images into two-dimensional slice images; the gene text features of each breast cancer patient are classified into N different sets; segmenting a two-dimensional slice image corresponding to the training set into a plurality of patches; each patch and the gene text feature corresponding to each patch are converted into a corresponding high-dimensional Embedding (high-dimensional Embedding); respectively multiplying H by Wk and Wv to obtain K and V; multiplying G by WQ to obtain Q; obtaining a gene guidance image feature CoAttn; the CoAttn and the Q are fused to obtain a feature set F; inputting the F into a network model, and training the network model based on a loss function; inputting the two-dimensional slice image corresponding to the test set into the trained network model, and outputting a plurality of classification results; and adopting a maximum voting strategy to make decisions on the plurality of classification results. According to the method, pCR and npCR classification is carried out on the patient based on a co-attention mechanism of gene text features and MRI images, the interpretability of model decision is enhanced, and a more visual and reliable auxiliary diagnosis tool is provided for clinicians.

The invention relates to a method for constructing a radio and television service fault diagnosis agent, which comprises the following steps of: 1, completing the construction of a basic agent project based on an MCP architecture, realizing the construction, deployment and association of three components, namely Host, Client and Server, of the MCP, and calling and packaging an existing diagnosis service API; step 2, realizing Qamp; the retrieval of the A document is enhanced and optimized, so that the response accuracy of an agent component MCP Host is improved; 3, based on the semantic slicing technology, retrieval enhancement optimization of a service fault diagnosis guide manual is achieved, and the decision accuracy of an agent component MCP Host is improved; 4, management, association, prompt word optimization and vectorization of an API document are achieved based on the semantic slicing technology, the description detail degree of a tool under an agent component MCP Server on an MCP Host is improved, and the hit rate and the accuracy rate of the MCP Server diagnosis tool are called; the problem that the manual service process of the current traditional radio and television service fault diagnosis scene is low in efficiency is solved, and the expansion of the service fault diagnosis agent scene is completed by using the natural language.

The invention relates to the field of disease risk prediction models, and discloses a thyroid cancer metastasis risk prediction model and a construction method and application thereof. The construction method of the model comprises the following steps: S1, collecting and preprocessing a sample; s2, gene expression quantity detection; s3, data set division and standardization processing; s4, constructing a deep learning model; s5, performing model evaluation; and the biomarker group for detecting the gene expression quantity in the S2 is composed of RPS4Y1, PKHD1L1, CRABP1, KRT18P8, AGPAT4, CPQ, SLC26A7 and RBP4. The prediction model can be used as a clinical auxiliary diagnosis tool and is used for guiding operation range selection, postoperative follow-up visit strategy making and early intervention of high-risk patients, and the precise diagnosis and treatment level of thyroid cancer is improved.

The application relates to the field of disease prediction, and particularly relates to an epilepsy prediction system based on electroencephalogram-electrogastrogram signals collected synchronously, which comprises the following modules: a data acquisition module, which is used for acquiring data, wherein the data comprises electroencephalogram data and electrogastrogram data; a first analysis module, which is used for obtaining average electrode inconsistency indexes of first channels and second channels based on a first model according to time sequence data; and a prediction module, which is used for comparing the average electrode inconsistency indexes of a subject with preset evaluation thresholds, so as to predict epilepsy patients. The system provided by the application can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be applied to the preliminary screening of epilepsy for a large population (for example, a community or a physical examination center) as an auxiliary diagnosis tool.

The application relates to the technical field of intelligent agriculture and plant protection, and particularly discloses a broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion, which comprises a portable collection platform, a mobile experiment terminal and a server. The portable collection platform is integrated with a hyperspectral imaging camera, a chlorophyll fluorescence imager, a dark-adapted leaf clamp and a camera for sample barcode recognition, and is provided with an adjustable height support and a controllable light source to ensure the consistency of data collection conditions. The mobile experiment terminal is internally provided with a physicochemical index rapid detection module, including a portable PCR instrument for on-site nucleic acid rapid extraction and amplification and a miniature ultraviolet spectrophotometer for rapid determination of pigment content. The broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion are highly integrated, portable and automated, are suitable for a field environment and provide an efficient disease diagnosis tool for non-expert users.

The invention discloses an interpretable machine learning method for predicting multi-organ injuries of diquat poisoning patients, and the method comprises the steps: constructing and verifying a prediction system of eight kinds of organ injuries through a clinical database; toxic encephalopathy, myocardial injury, acute kidney injury, circulatory failure, skeletal muscle injury, liver injury, lung injury and gastrointestinal tract injury are covered. According to the method, early-stage efficient prediction of multi-organ injury is achieved, deep insight is provided for potential biological mechanisms of organ injury, clinical formulation of individualized intervention strategies is facilitated, the prognosis management level of diquat poisoning patients is expected to be remarkably improved, and the incidence rate and case fatality rate of multi-organ failure are reduced. According to the technical scheme, high prediction precision is kept, meanwhile, balance of interpretability, stability and clinical applicability of the model is achieved, a reliable auxiliary diagnosis tool is provided for organ injury risk assessment of acute poisoning patients, and the method has important clinical application value and wide market prospects.

The present invention provides a magnetic contact comprising: a stationary core magnetized by a coil; a movable core spaced apart from the stationary core by a first distance (d1) and moved by the magnetic attraction of the magnetized stationary core; a longitudinal cross bar connected to the movable core; a movable contact carrier horizontally disposed on one side of the cross bar; a movable contact disposed on one surface of the movable contact carrier; a stationary contact spaced apart from the movable contact by a second distance (d2)<sb / >; and a longitudinal contact residual quantity diagnosis tool disposed upright on the movable contact carrier so as to be adjacent to the cross bar, wherein, when over travel of the cross bar represented by the difference between the first distance and the second distance occurs, the upper side of the contact residual quantity diagnosis tool opens to the outside at the same height as the value of the over traveling.

The invention discloses a system for endometriosis diagnosis and a computer storage medium. Aiming at major adjustment of endometriosis diagnosis standards, biomarkers and ploidy information of circulating endometrial cells in easily available samples such as peripheral blood of a patient are detected, and comprehensive risk assessment is performed by utilizing an optimization algorithm model, so that the diagnosis accuracy of endometriosis is improved. The invention provides an objective and quantifiable noninvasive diagnostic tool adapted to the adjusted standard, and the method solves the problem of missed diagnosis caused by false negative in the existing surgical diagnostic method, and has important clinical application value, social benefit and market prospect for improving the overall diagnostic level of endometriosis.

We provide the system. [Solution] Information collection methods for collecting users' biometric data, A signal transmission means for transmitting the aforementioned biometric data to an augmented reality platform, The platform includes a diagnostic means that analyzes the biometric data to evaluate the user's mental state, Activity generation means that generates activities optimized for the user based on the results of the diagnostic means, A presentation means for notifying the user of the generated activity through visual or audio feedback, A feedback collection means collects user responses to the aforementioned activities again using information gathering means and reflects them in subsequent optimization, A system that includes this.

A system is provided.SOLUTION: A system comprising: diagnostic means for diagnosing a technical level of a user; teaching material providing means for providing a teaching material and a task suitable for the user based on a result of the diagnostic means; evaluation means for evaluating a result of the task and generating feedback; and progress management means for managing learning progress of the user and providing a message and a reward for maintaining motivation.SELECTED DRAWING: Figure 1

The invention is suitable for the technical field of medical intelligent diagnosis and machine learning, and provides a pneumonia screening and risk prediction model construction method based on a reverse neural network, and the method comprises the steps: firstly carrying out the preprocessing of a bacterial pneumonia clinical symptom data set, and dividing a multi-classification problem into a plurality of binary classification tasks; secondly, constructing a CPT algorithm based on a random forest and a convex hull theory, calculating a feature weight through information gain, and screening out high-contribution symptom features; and finally, inputting the screened features into the optimized BPNN, and obtaining a prediction model through repeated iterative training. The method has good classification accuracy, the number of the selected features is obviously reduced compared with a traditional model, and the method can be used as a clinical auxiliary diagnosis tool, is applied to clinical bacterial pneumonia infection screening and severe risk early warning, and provides decision support for timely treatment.

The invention provides a multi-target joint detection kit for prostatic cancer, a detection system and application, multiple targets in the kit are selected from a PCAT1 gene, a PSA gene, a TTC3 gene, an EPCAM gene and an MIR4435 gene, and the kit further comprises a primer and probe combination aiming at each marker. According to the method, the diagnosis accuracy can be improved, the weight interpretation model can be constructed, the clinical decision can be optimized, and a non-invasive and high-compliance auxiliary diagnosis tool is provided for the problems of traumatic detection and gray areas in the prior art.

The invention discloses a sarcopenia assessment system and medical equipment, and the system comprises a data processing module which is used for splitting and preprocessing clinical data; the feature screening module is used for determining an optimal feature subset; the model building and training module is used for building and optimizing a sarcopenia evaluation model; the model evaluation module is used for evaluating the generalization performance of the model; and the practical application module is used for evaluating a new patient. Based on a bioelectrical impedance analysis technology, four key characteristics of gender, body weight, extracellular fluid resistance and intracellular fluid resistance are automatically screened out, and a multi-model preferential strategy is adopted to construct a high-performance evaluation model. According to the method, the defects that a traditional method depends on expensive equipment, complicated processes, subjective indexes and the like are overcome, rapid, objective and accurate screening of sarcopenia is achieved, and an efficient and reliable auxiliary diagnosis tool is provided for clinic.

The present application relates to the field of disease prediction, and particularly relates to a cognitive impairment prediction system based on synchronous acquisition of electroencephalogram and electro-gastrogram signals, comprising: a data acquisition module, configured to acquire data, wherein the data comprises electroencephalogram data and electro-gastrogram data; a first analysis module, configured to obtain average electrode inconsistency indexes of first and second channels based on a first model according to time series data; and a prediction module, configured to compare the average electrode inconsistency indexes of the subject with preset evaluation thresholds, thereby predicting cognitive impairment patients. The system provided by the present application can more accurately and efficiently distinguish mild cognitive impairment patients from healthy people, and can be applied as an auxiliary diagnosis tool for the initial screening of mild cognitive impairment in a large population (for example, a community or a physical examination center).

A disease diagnosis tool validates a diagnosis of a patient. The disease diagnosis tool receives a high-resolution image of a body part. The disease diagnosis tool divides the high-resolution image into a plurality of tiles and inputs a representation of each tile into an encoder portion of a model configured to perform a disease diagnosis based on the representations. The encoder has an attention mechanism. The disease diagnosis tool obtains a plurality of tokens representative of an attention of the encoder based on the attention mechanism. Each token is associated with a position of a tile of the plurality of tiles. The disease diagnosis tool generates a heat map corresponding to the image of the body part. The heat map comprises a two-dimensional image having pixels corresponding to tiles each with an amplitude based on a level of attention represented in the plurality of tokens corresponding to the tiles.

The invention discloses a gene marker combination, a kit and a system for breast cancer molecular typing, and belongs to the field of precise medical big data analysis. The gene marker combination is composed of 17 gene markers, namely, CLDN19, DSG1, FABP7, FGFBP1, KLK7, KLK8, MAPK4, MAPT-AS1, MAPT-IT1, MIA, PTPRZ1, ROPN1, ROPN1B, SCN2B, SERPINA11, SOX11, and SRARP. The invention further discloses a preparation method of the gene marker combination. Based on the combination, a brand new breast cancer GPS17 molecular typing system is constructed, the breast cancer can be finely divided into eight molecular subtypes with remarkable expression difference, and at least four different molecular subgroups in the Luminal A subtype are systematically analyzed for the first time. Experiments show that the molecular typing system is obviously superior to a traditional PAM50 molecular typing system in balance accuracy, macro F1 score and macro AUC score. The invention further provides a corresponding detection kit and an automatic typing system integrated with a machine learning model, and an auxiliary diagnosis tool with excellent performance is provided for accurate diagnosis and treatment decision of breast cancer.

The application discloses a visual AI auxiliary writing and intelligent diagnosis system and method for quantification strategy learning, and belongs to the technical field of financial technology and artificial intelligence. The system comprises: a teaching AI strategy generation module, which analyzes natural language strategy description into a three-layer structure of a strategy skeleton layer, a condition logic layer and a parameter configuration layer, generates a structured strategy description language intermediate representation with three-layer teaching annotations of logic annotations, variant prompts and learning point annotations, and converts the structured strategy description language intermediate representation into target real-time environment code; a learning path adaptive scheduling module, which adopts deep reinforcement learning dynamic planning personalized learning paths based on user multi-dimensional capability portraits and strategy difficulty quantification models; a strategy risk diagnosis and market adaptation module, which generates a strategy multi-dimensional risk portrait, calculates strategy-market adaptation degree scores and triggers early warnings; a strategy variant intelligent generation module, which generates diversified strategy variants based on a genetic algorithm and analyzes parameter-performance influence modes through comparative learning; and a multi-agent collaborative strategy diagnosis module, which performs parallel diagnosis by four special agents of code auditing, overfitting detection, market sensitivity analysis and signal quality evaluation, performs causal reasoning by an arbitration agent, and generates a natural language diagnosis report. The application solves the problems of poor code learnability, incompatible learning and real-time code, lack of personalized path guidance and intelligent diagnosis tools in existing quantification strategy learning platforms, and significantly reduces the learning threshold of quantification strategies.

The invention belongs to the technical field of intelligent household appliances, and particularly relates to a household appliance fault processing method and device and a storage medium. According to the method, fault processing data of the household appliance is acquired through a cloud server, and the fault processing data at least comprises one or more of the following items: data reported by the household appliance, detection data for detecting the household appliance by adopting a fault diagnosis tool, and fault field confirmation data; inputting the fault processing data into a pre-trained fault recognition model to obtain a first fault recognition result output by the fault recognition model, the first fault recognition result comprising a fault type, one or more fault causes, confidence of each fault cause, and a processing measure corresponding to each fault cause, the fault identification result is sent to the fault processing terminal; according to the method, the fault processing efficiency of the household appliance is improved, and the user experience is improved.

The invention discloses a CTSNet model construction method for grading the severity of a carpal tunnel syndrome, and the method is characterized in that a CTSNet backbone network, an Attention Pooling attention fusion layer and a classification layer which are sequentially cascaded are adopted to construct a CTSNet model for intelligently grading the carpal tunnel syndrome through multi-modal electrophysiological signals, five multi-modal time-frequency diagrams of a patient are used as model input, and the model input is used as model output. According to the method, CTS intelligent grading based on time-frequency graph deep learning is realized, and a CTSNet backbone network is a residual structure integrating a frequency attention mechanism and optimization and comprises a 7 * 7 initial convolution layer, a BatchNorm2d batch normalization layer, a ReLU activation function, a 3 * 3 maximum pooling layer and four CTS special residual blocks which are cascaded in sequence. Compared with the prior art, the method has the advantages that different development stages of CTS can be accurately recognized, the accuracy, fineness and reliability of CTS grading are remarkably improved, the performance of the model on different patient groups and equipment is more stable through a two-stage training strategy and a residual framework, and a more effective intelligent auxiliary diagnosis tool is provided for clinical practice.

The invention discloses a method for optimizing a memory tool. The method comprises a development stage, a test stage for preventing a memory problem from being introduced, and a test stage, wherein the memory problem missed in the development stage is found; in the construction and deployment stage, the memory problem of the production environment is monitored and quickly responded; establishing a problem checking process and a knowledge base; and accurately positioning and radically treating the problem. According to the method, a problem finding stage is advanced to a development and test stage as far as possible, the problem finding stage is solidified into a research and development process through an automatic tool chain, and meanwhile, a powerful monitoring and diagnosis tool is provided for a production environment, so that a complete closed loop from'prevention '-gt, 'discovery'-gt, 'monitoring '-gt and'radical treatment' is formed.

The invention provides a fault diagnosis method and computing equipment. The fault diagnosis method comprises the following steps: acquiring a fault diagnosis request; wherein the fault diagnosis request is used for requesting to perform fault diagnosis on to-be-diagnosed equipment in a target diagnosis scene; determining a target diagnosis toolkit for performing fault diagnosis on the to-be-diagnosed equipment according to the target diagnosis scene and the equipment model of the to-be-diagnosed equipment; generating a target diagnosis mirror image based on the target diagnosis toolkit and the initial diagnosis mirror image; deploying a target diagnosis mirror image on the to-be-diagnosed equipment to perform fault diagnosis on the to-be-diagnosed equipment to obtain a target diagnosis result of the to-be-diagnosed equipment; thus, fault diagnosis is converted from manual operation to automation and precision, the diagnosis accuracy is improved, meanwhile, the cost is reduced, and the method can adapt to complex scenes.

The invention relates to the technical field of intelligent agriculture and plant protection, and particularly discloses a broccoli black spot early diagnosis system and method based on multi-mode machine learning fusion, and the system comprises a portable collection platform which is integrated with a hyperspectral imaging camera, a chlorophyll fluorescence imager and a dark adaptation leaf clamp, the platform is provided with a height-adjustable bracket and a controllable light source, so that the consistency of data acquisition conditions is ensured; a physical and chemical index rapid detection module is arranged in the mobile experiment terminal, and the mobile experiment terminal comprises a portable PCR instrument for rapid extraction and amplification of nucleic acid on site and a miniature ultraviolet spectrophotometer for rapid determination of pigment content. According to the broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion, the system is highly integrated, portable and automatic, is suitable for a field environment, and provides an efficient disease diagnosis tool for non-expert users.

The invention provides a chronic obstructive pulmonary screening auxiliary diagnosis system based on multi-source data, and belongs to the technical field of medical diagnosis, and the system comprises a data collection module which is used for collecting related data of a patient, and the related data comprises a chest CT image and a lung function index; the lung function index comprises a ventilation function index; the data construction module responds to the related data and is used for constructing at least six emphysema observation points according to the acquired chest CT images and acquiring CT images corresponding to the emphysema observation points from the acquired chest CT images; and the feature extraction module is used for extracting features in the CT images corresponding to the emphysema observation points, and the extracted features comprise morphological features. Through multi-source data fusion, dynamic monitoring and quantitative analysis, the accuracy and efficiency of chronic obstructive pulmonary disease screening are improved, and a scientific and reliable auxiliary diagnosis tool is provided for clinic.

The application discloses an optoelectronic equipment health management system based on digital twinning, which comprises an optoelectronic equipment health management system, a portable diagnostic tool ATE and an in-field health management training platform; wherein the optoelectronic equipment health management system comprises a real-time state monitoring module, a fault diagnosis module, a fault prediction module and the like; the portable diagnostic tool ATE is used for deep fault diagnosis, online maintenance, data collection, expert algorithm model updating and parameter updating; the in-field health management training platform is used for supporting the optimization iteration of intelligent algorithm models and parameters of fault prediction and diagnosis, health management, task decision and support decision, and the in-field model training is completed by connecting the in-field health management training platform with the portable diagnostic tool ATE brought back from the field, importing the state data and health management process data of the field optoelectronic equipment, and the like. The application can accurately predict the health state of the field optoelectronic equipment, accurately generate a maintenance time and a maintenance strategy, and effectively solves the problem of optoelectronic equipment health management.

The invention discloses a railway signal cable intelligent monitoring system which comprises a cable on-line monitoring system, an insulation resistance monitoring device, a cable fault and positioning monitoring device, a cable identification device, a high-resistance bridge monitoring device, a cable forming end detection device and a line selection device. The method has the beneficial effects that the automation level of fault detection and processing can be improved by using a real-time online monitoring technology and a method of automatically triggering fault diagnosis through an automatic fault monitoring and diagnosis technology; through the single-end remote monitoring technology, the advanced sensor and monitoring technology are adopted, remote single-end online monitoring of the railway cable is achieved, and the limitation that double-end or multi-point monitoring is needed in the prior art is broken through; through an intelligent fault positioning technology, advanced modes such as a low-voltage pulse method and a high-resistance bridge method are developed, fault points are accurately and rapidly positioned in cooperation with an auxiliary diagnosis tool, and the fault processing precision and efficiency are improved.

The invention discloses a malignant tumor Hamp; the invention relates to an automatic identification and quantification device for a nuclear division image of an E-stained pathological section. E, dyeing the full-slice digital image, identifying a tumor area on the full-slice digital image, and labeling a hot spot area with enriched nuclear split images for the tumor area; cutting the hot spot region into patch images of a preset size, and detecting the patch images according to the trained target YOLOv8 model to obtain a bounding box and a confidence coefficient of a nuclear split image; according to the method, a visualized annotation image and a structured data file are generated, the visualized annotation image and the structured data file are in butt joint with a pathological information system, a grading suggestion text is automatically generated, high-precision detection of the nuclear split image can be achieved, the whole process of quantitative analysis of the nuclear split image is automated, pathological diagnosis efficiency and clinical decision reliability are improved, and the clinical diagnosis efficiency is improved. A visual and reliable auxiliary diagnosis tool is provided for pathologists, the pathological diagnosis efficiency is improved, and the cross-mechanism generalization ability and the clinical integration and standardization output ability are enhanced.

The utility model discloses a low temperature valve on-line leak hunting diagnosis tool, which comprises a valve part, a rotating part, an isolation part and a plug part, the rotating part is arranged at the top of the valve part, and a rotating shaft of the rotating part is perpendicular to the axis of the valve part; the isolation part is arranged in the valve part, divides the interior of the valve part into an upper part and a lower part and comprises a through hole, the through hole communicates with the upper part and the lower part of the valve part, the through hole and the rotating part are coaxially arranged, and a circumferential baffle ring is arranged on the hole wall of the lower portion of the through hole; the plug head part comprises a plug head body and a plugging assembly, the plug head body is arranged at the bottom of the rotating part, the plugging assembly is arranged on the outer ring of the plug head body and used for sealing the upper surface of the baffle ring, and the plug head body below the plugging assembly is used for sealing the baffle ring. The chock plug body below the plugging assembly can seal the inner hole of the baffle ring, and primary sealing is provided; the upper surface of the baffle ring can be sealed by the plugging assembly, secondary sealing is provided, and the sealing performance of the low-temperature valve on-line leak hunting diagnosis tool is further enhanced.

The application provides a kind of diabetes nephropathy auxiliary identification method and system based on CT image, it is related to medical image processing and computer-aided diagnosis technical field, the method comprises: obtaining the abdominal CT image of target object;Segment kidney, perirenal fat and body composition region of interest;Extract radiomics features;Screening to obtain target radiomics features;Input radiomics model to obtain whether target object is identified result of suffering from diabetes nephropathy.The application extracts microcosmic image markers related to renal function damage from conventional CT images by comprehensively using multiple site radiomics features, realizes non-invasive, accurate auxiliary identification of diabetes nephropathy, significantly improves detection efficiency, and provides a reliable image-aided diagnosis tool for clinic.

The invention belongs to the technical field of medical image processing and artificial intelligence, and particularly relates to a pathological image intelligent diagnosis method based on deep learning, comprising the following steps: S1, acquiring a to-be-diagnosed pathological image; s2, preprocessing the pathological image; S3, carrying out image segmentation on the preprocessed pathological image; s4, inputting the to-be-analyzed area of the pathological tissue into a pre-trained deep learning diagnosis model, and performing feature extraction and classification prediction; s5, when the confidence coefficient is greater than a preset threshold value, generating pathological diagnosis mark information at a corresponding position in the pathological image; s6, outputting a pathological image containing pathological diagnosis mark information; the pathological image features are automatically extracted through the deep learning model, so that the dependence on artificial experience is reduced; a lesion area can be automatically positioned and marked in a large-size pathological image; the efficiency and the consistency of pathological diagnosis are improved; the method can be used as an auxiliary diagnosis tool to provide reliable reference for pathologists.