54results about How to "Good diagnosis" patented technology

Traumatic brain injury (TBI) in a patient is assessed. A TBI diagnosis for the patient is determined based on features from MRI data, such as anatomical features, functional features, diffusion features, connectivity features from functional MRI, connectivity features from diffusion MRI, and/or network features from the connectivity features. The TBI diagnosis is determined using a trained classifier. The classifier synthesizes the features into a single number (e.g., a confidence in the prediction of the diagnosis) and indicates the features most responsible for the diagnosis. The disease trajectory for a given patient may be predicted using the trained classifier.

An ultrasonic array probe is fixed to a rotational shaft at a predetermined angle, and thus a mechanical structure is made simple. An ultrasonic beam is electronically controlled so that an ultrasonic transmission/reception direction may become substantially perpendicular to the surface of the mamma. Thereby, data on the entire mamma including a C′ region can be collected only by the rotation of the probe. In addition, a membrane which is interposed between the probe and the mamma is formed to have a mesh-like structure, thereby reducing multiple reflection. Moreover, a B mode image and a C mode image are displayed at the same time, and thereby an accurate diagnosis can be performed in a short time.

The invention discloses a fault diagnosis method based on a signed directed graph and data constitution. The fault diagnosis method includes the steps of firstly, collecting normal data to conduct offline training, decomposing pre-processed data through a PCA, and solving a control limit of an SPE; secondly, setting up an SDG model according to a system flow graph, and determining parameters of all variables V-mask after setting the missing alarm rate, the false alarm rate and the detection offset; thirdly, collecting process data of system unknown state in real time, monitoring a CUSUM of all the variables and the SPE of a sample, if the SPE exceeds the control limit, diagnosing that a fault happens to a system, determining an effective node through the CUSUM, and conducting reconstitution on data in all compatible path directions by searching for all possible compatible paths, wherein the direction with the largest fault isolation index is taken as a real fault propagation direction, the starting node in the direction is taken as a causal variable of the fault, and an event which causes the node to be abnormal is taken as the root cause of the fault.

The invention discloses a test strip for fluorescence immunochromatography of myeloperoxidase. The test strip comprises a base plate, a sample pad, a binding pad, a nitrocellulose membrane and an absorbent pad, and the sample pad, the binding pad, the nitrocellulose membrane and the absorbent pad are assembled on the base plate in a sequential overlapping manner, wherein the absorbent pad and thebinding pad are respectively pressed on two ends of the nitrocellulose membrane in an overlapping manner, and a detection area is formed on the surface of the nitrocellulose membrane; the sample pad is pressed on the binding pad in an overlapping manner, and a myeloperoxidase antibody-fluorescent microsphere compound is immobilized on the binding pad; and the nitrocellulose membrane in the detection area is coated with a detection line formed by a monoclonal antibody for recognizing another epitope of myeloperoxidase and a control line formed by a goat anti-mouse IgG polyclonal antibody. The test strip has the advantages of high sensitivity, high stability, realization of the detection linearity being 3.125-600 ng/ml, no non-specificity, short detection time of 5 min, realization of bedside quick test, and great improvement of the clinical diagnosis efficiency.

The invention provides a breast tumor diagnosis system based on magnetic resonance spectrum imaging, comprising a superconduction MR (Magneto Resistance) scanner and a computer system. Through a computer auxiliary detection measure, the breast tumor diagnosis system realizes the early identification diagnosis of a breast tumor and the further diagnosis of breast cancer. Mammary gland magnetic resonance spectrum data are studied by adopting a distinctive manifold study method and projected to a low dimension embedding space, thus not only a low dimension manifold structure hidden in a high dimension magnetic resonance spectrum space can be revealed, but also the identification information in the mammary gland magnetic resonance spectrum data can be efficiently kept; then optimization clustering is carried out on the low dimension identification characteristic by utilizing a clustering method so that data points without the similar identification characteristic are separated to the maximum; and further a cost sensitive mechanism is introduced to achieve misclassification total cost minimization and realize optimization diagnosis of the breast cancer.

The invention discloses a fault online diagnosis method of uninsulated track circuit tuning zone, which relates to the online diagnosis of a track circuit. The method comprises the steps of obtaining cab signal inductive voltage amplitude envelope, shunt point positions, train speed and track circuit signal carrier frequency information, pretreating the data to obtain the length of the track circuit, the quantity and position of compensation capacitors and cab signal inductive voltage normalized amplitude envelope, performing segmented fitting on the cab signal inductive voltage normalized amplitude envelope and establishing corresponding feature vectors, establishing a neural network, training the neural network by utilizing the feature vectors and performing fault diagnosis on the uninsulated track circuit tuning zone equipment, and sending diagnosis results of the tuning zone equipment to a corresponding ground monitoring client-side through a cab signal remote monitoring system. The method has the characteristics of being complete in functions, high in detection accuracy, high in applicability, simple in method, good in real-timeness and the like, no any hardware equipment is added in the method based on the existing cab signal system, and the cost is low.

The invention discloses a thyroid nodule diagnosis method based on a deep learning network, and belongs to the field of image processing and artificial intelligence aided disease diagnosis. The methodcomprises the following steps: searching an ultrasonic original image and a pathological report of a thyroid nodule of a thyroid patient, and constructing a thyroid nodule database; preprocessing theultrasonic image; performing semantic segmentation on the ultrasonic image preprocessed in the step 2 through a Deeplab v3+ method based on Xception-JFT, and forming a semantic segmentation result graph; judging benign and malignant thyroid nodules based on a deep learning network; and forming a thyroid nodule diagnosis information report. According to the method, the Deeplab v3+ algorithm basedon Xeption-JFT is adopted to establish the thyroid ultrasound image segmentation network model, the optimal segmentation effect is achieved by continuously improving the backbone network Xception, nodule information can be automatically and rapidly recognized under high accuracy and high robustness, image features are automatically extracted for accurate segmentation to obtain a better diagnosis result, and an objective reference is provided for clinical diagnosis.

The invention discloses a diagnosis method and a system of the enterprise competitiveness. The method is characterized in that the method includes following steps: step of establishing a diagnosis system: establishing a three-grade diagnosis system of enterprise competitiveness diagnosis; step of establishing sub-databases formed by diagnosis problem description options related to three-grade diagnosis index factors and corresponding diagnosis state options and a calibration database; step of defining each grade of diagnosis indexes; step of defining the sub-databases; step of making an enterprise competitiveness diagnosis process from human-machine dialogue to diagnosis structure display; step of designing a random display device; step of designing a calibration device; step of designing a grading diagnosis device; step of establishing a competitiveness diagnosis result radar chart; and step of establishing a competitiveness diagnosis conclusion report. According to the method and the system, based on Porter industry competitiveness diagnosis theory, convenient, comprehensive, and systematic competitiveness diagnosis tools are provided for people in need of enterprise competitiveness or enterprise state, innovation and accuracy are provided, and the market prospect is wide.

The invention provides an aero-engine gas path fault fusion diagnosis method based on statistical distribution characteristics, a matching diagnosis module based on parameter deviation statistical characteristics is constructed, and diagnosis results are fused by adopting a weighted D-S evidence theory based on fault sensitivity; adverse effects of engine volume effect, metal heat transfer and measurement noise and offset on engine fault diagnosis are reduced and even eliminated, and the accuracy and reliability of engine fault diagnosis are improved; in order to solve the problem of multi-measurement parameter fusion conflict, the reliability of different measurement parameters is obtained through fault sensitivity analysis, so that different weight coefficients are given to fault diagnosis results of different measurement parameter statistical distribution characteristics in the fusion process, and the diagnosis reliability is improved.

The present invention discloses an OLTC mechanical fault diagnosis method based on MPE and SVM. The method comprises the following steps of: 1) performing collection of vibration signals of an on-loadtap-switch (OLTC) in a normal state and vibration signals of the OLTC in fault state through an acceleration sensor to perform preprocessing of the vibration signals; 2) performing multi-scale permutation entropy (MPE) calculation of the collected vibration signals, and constructing feature vectors taken as input of a support vector machine (SVM); and 3) inputting the feature vectors obtained inthe step 2) into the SVM to perform training of the SVM, and inputting test data into the trained SVM to determine a fault mode of the OLTC. The OLTC mechanical fault diagnosis method based on the MPEand the SVM does not need a lot of data for training of the SVM and is higher in diagnosis precision, and is obviously better than a BP neural network in the diagnosis effect of the OLTC.

The invention relates to the technical field of biology, and concretely discloses application of echinococcus granulosa glutaredoxin-1 to preparation of an immunizing antigen and/or a kit for detecting echinococcosis granulose. When being used as an immunizing antigen, the echinococcus granulosa glutaredoxin-1 can be recognized by sheep positive blood serum naturally infecting the echinococcosis granulose. When being applied to indirect ELISA detection, the echinococcus granulosa glutaredoxin-1 has high specificity and sensitivity; the clinic detection coincidence rate is as high as 97.9 percent. The detection method built by using the echinococcus granulosa glutaredoxin-1 as the immunizing antigen has a good diagnosis effect, and can be used for the primary screening of the echinococcus granulosa of sheep in an infected area.

The invention discloses an automobile diagnosis system and a method thereof. The system comprises an upper computer and a lower computer. The upper computer and the lower computer are connected through wired or wireless communication. The upper computer is provided with a user application program. The lower computer is provided with an automobile diagnosis program. The lower computer interacts with an automobile to detected according to a diagnosis instruction acquired by the user application program of the upper computer so as to acquire a diagnosis signal. The automobile diagnosis program calls a corresponding algorithm stored in the lower computer and automobile text data to carry out operation on the diagnosis signal so as to acquire a diagnosis result and feeds back the diagnosis result to the upper computer. In the automobile diagnosis system and the method, an automobile diagnosis operation process is performed in the lower computer, the upper computer is used for acquiring the diagnosis instruction and displaying the diagnosis result, the upper computer adopts electronic equipment possessing input, display and communication functions and there are various forms; and based on a system platform of the lower computer, a set of diagnosis program is developed, research and development cost is low and the program is not easy to crack.

The invention relates to the technical field of medical instruments, in particular to a portable medical bowel sound analyzing, storing and playing system. The portable medical bowel sound analyzing, storing and playing system comprises a microphone, a controller, a memory and a player, wherein the microphone, the memory and the player are electrically connected with the controller; the microphone is used for converting a bowel sound signal into an electric signal and transferring the electric signal to the controller; the controller resolves the electric signal and then converts the electric signal into a sound file, and the sound file is stored in the memory; the player is used for playing the sound file. The portable medical bowel sound analyzing, storing and playing system provided by the invention facilitates recycling and auscultation and contributes to simultaneous acquisition of the bowel sound information of a plurality of patients and cleaning of complete bowel sound information, a doctor can perform better diagnosis, the diagnosis efficiency is high, and a great amount of time is saved; moreover, the portable medical bowel sound analyzing, storing and playing system is particularly suitable for bowel sound monitoring of people suffering from infectious diseases, the doctor does not need to be in direct contact with the patients, and the infection probability of medical staff is reduced.

The invention relates to a remote monitoring system and method for a pipeline diaphragm pump based on a wireless network and GPRS, and belongs to the field of remote monitoring. The present invention first collects the vibration information of the diaphragm pump, and then transmits the information to the data analysis connector after being processed by the single-chip microcomputer. The PC in the data analysis connector analyzes and processes the vibration signal, and then uses the GPRS wireless transmission module to transmit the information to the Remote monitoring platform, when the vibration of the pipeline diaphragm pump is found to be faulty, the user can send control commands to the pipeline diaphragm pump, and the user can realize remote monitoring of the pipeline diaphragm pump through the use of the remote monitoring platform. The invention has the characteristics of convenience and high efficiency. It can effectively monitor the operation of the pipeline diaphragm pump in real time, and can ensure that when the vibration of the pipeline diaphragm pump fails, it can stop running in time, ensure the safe transportation of the pipeline, and facilitate the production safety of the enterprise. At the same time, the overall structure is simple. , work is stable and easy to promote.

The invention discloses a placenta protein 13 test strip and a quantitative detection method, and is characterized in that the test strip comprises a PVC base plate, the PVC base plate is sequentially provided with a sample pad, a combination pad, a nitrocellulose membrane and a water absorption pad, the sample pad is lapped on the combination pad, the combination pad and the water absorption pad are lapped at two ends of the nitrocellulose membrane respectively, an anti-placenta protein 13 antibody marked by a tracer and a quality control line ligand 1 marked by the tracer are fixed on the combination pad; and the nitrocellulose membrane is provided with a detection line and a quality control line, the detection line is fixedly provided with an antibody for resisting another epitope of the placenta protein 13, and the quality control line is fixedly provided with a ligand 2 which can be specifically combined with a ligand 1 of the quality control line.

A transformer fault diagnosis method and system using induced ordered weighted evidence reasoning is provided. The method includes the following steps. A typical data sample of transformer sweep frequency response analysis is loaded and a diagnostic label is set as an identification framework. Test data of a device to be diagnosed is loaded. Basic probability assignment is calculated and a reliability decision matrix is constructed. An induced ordered weighted averaging operator and its induction vector are calculated according to a sample source of the data. An index weight vector is calculated. All evidence is fused by the induced ordered weighted evidence theory and reliability of comprehensive evaluation is calculated, so as to determine a diagnosis result. The disclosure realizes fault identification, fault type distinction and fault position of power equipment by interpreting detection waveforms.

The invention discloses a digital circuit board self-diagnosis system based on the feature compression technique. The digital circuit board self-diagnosis system based on the feature compression technique comprises a test vector generating module, a space compression module, a time compression module and a diagnosis module, wherein test vectors are added to N modules to be tested of a circuit board respectively through the test vector generating module, so that test response K*K-order square matrix data are obtained; the test response K*K-order square matrix data are added according to phase modes through the space compression module, so that 2K odd-even check values are obtained; time compression is conducted on the 2K odd-even check values through the time compression module so that a line detection value and a row detection value are obtained; the line detection value and the row detection value are compared with a prestored standard line detection value and a prestored standard row detection value respectively through the diagnosis module, and the position information of a corresponding faulted module is displayed according to the line detection value and the row detection value when the line detection value and the row detection value are not equal to the prestored standard line detection value and the prestored standard row detection value respectively. The invention further provides a digital circuit board self-diagnosis method based on the feature compression technique.

The invention discloses a novel molecular marker and application thereof in preparing a kit for diagnosis and prognosis of head and neck cancer. RNA (Ribonucleic Acid) sequences of the novel molecularmarker are shown as SEQ ID NO:1 to SEQ ID NO:48. A mathematical model for diagnosing the head and neck cancer is constructed by taking the marker disclosed by the invention as a basis, and has the advantages of high sensitivity, good specificity and good diagnosis effect; AUC can be as high as 0.981; besides, 48 tRF fragments can be used as molecular markers for typing the head and neck cancer and predicting life expectancy of patients; head and neck cancer tumor samples are clustered into four subtypes according to tRFs expression in test data; the analysis of a survival curve shows that thelife expectancies of the subtypes have significant difference. The novel tRF molecular marker disclosed by the invention has good characteristics of diagnostic indicators and higher clinical use andpopularization value, and can be effectively used for diagnosis, typing and prognosis of the head and neck cancer.

The invention discloses application of a novel molecular marker to the preparation of a kit for diagnosis and prognosis of renal clear cell carcinoma. The RNA sequence of the novel molecular marker isshown in SEQ ID NO: 1 to SEQ ID NO: 57. A mathematical model for diagnosis of renal clear cell carcinoma is constructed by taking the marker as a base; the model is high in sensitivity and good in specificity, AUC can be as high as 0.997, and the diagnosis effect is good. In addition, 57 tRF fragments can serve molecular markers for classifying renal clear cell carcinoma and predicating the survival periods of patients; in test data, a renal clear cell carcinoma tumor sample is clustered into 3 subtypes according to rRFs expression, and survivorship curve analysis shows that the survival periods of the subtypes have obvious difference. The novel tRF molecular marker has excellent diagnosis index characteristic, can be effectively applied to the diagnosis, classification and prognosis of renal clear cell carcinoma, and has high clinical application and promotion values.

The invention discloses a device for automatically diagnosing orthopedic diseases based on medical image information. The device is characterized by comprising a medical image acquisition module, an identification module, an orthopedic knowledge database, a diagnosis inference engine and an output module. The medical image acquisition module is used for acquiring medical image information; the identification module is used for analyzing the medical image information and identifying and labeling entities and relationships contained in the medical image information; the orthopedics knowledge database is used for storing an orthopedics knowledge graph containing orthopedics entities and relations; the diagnosis inference engine is used for combining the entities and the relationships identified in the medical image information with the orthopaedic knowledge graph and carrying out reasoning judgment to obtain a diagnosis result; and the output module is used for outputting the diagnosis result. The device can significantly improve the accuracy and efficiency of orthopedic disease diagnosis.

The invention discloses a transformer winding vibration signal identification method based on ITD permutation entropy and CGWO-SVM. The transformer winding vibration signal identification method comprises the steps that vibration signals of a transformer winding in normal and fault states in the switching process are collected; and ITD is adopted to decompose a vibration signal, and permutation entropy is utilized to perform complex calculation on the decomposed signal to extract a feature vector, and the feature vector is used as an input quantity of CWGO-SVM for training, and the feature vector is compared with GWO-SVM and SVM. The transformer winding vibration signal identification method can effectively extract the fault features of the transformer winding, is high in diagnosis resultprecision, achieves the intelligent diagnosis of the fault of the transformer winding, can find the fault at the early stage of a latent fault, is higher in operability, and is superior to a conventional SVM in technical effect.

The invention discloses an image scanning pathological section system which comprises a scanning acquisition module, a data storage module, a PLC control module, an image extracting module, an image contrasting module, an image analysis result module and a signal output interface module. The scanning acquisition module transmits the acquired image information to the PLC control module through thedata storage module. The PLC control module transmits a control signal to the image contrasting module through the image extracting module. The image contrasting module transmits contrasting information to the image analysis result module. The PLC control module controls signal outputting of the signal output interface module. The image scanning pathological section system according to the invention has advantages of high real-time performance, accurate diagnosis result and high diagnosis effect.

The invention discloses a bearing fault degree identifying method based on a flow diagram and a non-nave Bayesian inference. The identifying method comprises the following steps: step one, extractingfault diagnosis features of a roller bearing in a training sample, and used for constructing a standardized flow diagram; step two, deleting a redundant sign attribute node in the standardized flow diagram by using a node reduction algorithm based on sign attribute node importance, and acquiring the flow diagram after node reduction; and step three, extracting the fault diagnosis features of the roller bearing in a sample to be detected, and identifying a state of the roller bearing in the sample to be detected by using a non-nave Bayesian inference algorithm based on the flow diagram. The method is capable of intuitively and accurately identifying a fault degree of the roller bearing, and providing a novel solution idea for fault degree identification of the roller bearing.

The invention relates to a novel application of small molecule metabolites ethanolamine, fumaric acid, hypotaurine and inositol in a serum sample as a combined marker in preparation of a kit for diagnosing whether a diabetic has retinopathy or not. The invention also relates to a kit for detecting retinopathy in diabetic patients, which is used for calculating the variable Prob value of the combined marker and judging the cut-off value (cut-off value) on the basis of a binary logistic regression equation by detecting the respective relative concentrations of the combined marker in the serum of the diabetic patients, and judging whether the diabetic suffers from retinal complications or not. The kit can realize high-sensitivity and high-efficiency detection of several small molecule metabolites involved in the invention, and has the characteristics of low detection cost and good repeatability. The method can be applied to auxiliary diagnosis of retinal complications in diabetic people, has a good effect in early diagnosis of diabetic retinopathy, and has a good application prospect.

The invention discloses an immunochromatography quantitative detection test paper strip for procalcitonin of terminal blood. A base plate of the test paper strip is sequentially provided with a sample pad, a nitrocellulose film and a water absorbing pad; the sample pad is composed of a glass cellulose film and a polyester film, and anti-human erythrocyte antibodies, biotin-labeled PCT monoclonal antibodies and streptavidin-labeled fluorescin are immobilized on the sample pad; a detection line composed of monoclonal antibodies for recognizing another epitope of PCT and a quality control line composed of polyclonal antibodies are immobilized on the nitrocellulose film. The test paper strip is simple in structure and can detect PCT of the terminal blood.