179results about How to "Reduce misdiagnosis" patented technology

A method and apparatus for diagnosing schizophrenia, schizophrenia disorder subgroup, or predisposition thereto in a test subject is disclosed. The method includes the steps of determining an interhemispheric switch rate of the test subject. In one embodiment, the interhemispheric switch rate of the test subject is under conditions of increasing rate of dichoptic reveral, and comparing the switch rate with a corresponding reference switch rate to diagnose presence or absence of schizophrenia, a schizophrenic disorder subgroup, or predisposition thereto. In a preferred embodiment, the interhemispheric switch rate is determined by measuring the rate of binocular rivalry in the test subject. Also disclosed is use of a diagnostic method in genetic linkage studies for the identification of the molecular defect(s) underlying schizophrenia, and for the identification of compounds which may alleviate the disorder.

The invention discloses a method for establishing a medical image atlas based on image segmentation and the CNN. A CNN method is used to segment medical images finely, image segmentation identification is carried out on the collected medical images, medical knowledge unit segmentation is carried out on medical diagnosis knowledge, and knowledge units of different medical images are formed; an image relation of image segmentation is analyzed to connect the different image knowledge units and communicate with an additional medical illness knowledge unit, a knowledge base association relation isestablished on the basis of connection between medical image identification and medical diagnosis knowledge; characteristic matching is carried out on the knowledge units after image segmentation, entities with the same type of attribute serves as entity nodes in the knowledge atlas, a knowledge base entity is established by fusion, and entities are aligned; and a knowledge atlas is established byusing the entities and the associations therebetween. Suspected diagnosis prompt and treatment suggestion are provided, and AI diagnosis is made possible.

Methods, computer systems, and computer readable medium for testing a plurality of models in order to classify a biological specimen. A determination is made as to whether a model precondition associated with a model in the plurality of models has been satisfied. The model is skipped when the model precondition has not been satisfied. When the model precondition has been satisfied, the first model is tested by selecting a calculation from the first model. The selected calculation is computed using cellular constituent abundance values from the biological specimen in accordance with a calculation algorithm set forth in the selected model. This selecting and computing is repeated for one or more calculations in the selected model. Then, each of these calculations is aggregated in accordance with an aggregation algorithm associated with the selected model. These steps are repeated for at least one other model in the plurality of models. A computer medium comprising a classifier for classifying a biological specimen into a biological sample class that includes one or more models. Each of the one or more models comprises (i) an optional model precondition, (ii) a calculation algorithm, (iii) a plurality of calculations, and (iv) a calculation aggregation algorithm.

The invention discloses a medical image interpretation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an image analysis request which comprises a target medical image; adopting a pre-trained image recognition model to recognize the target medical image, and obtaining a feature map output by the last convolutional layer in the image recognition model; based on the feature map, obtaining a prediction probability value corresponding to each original focus category output by the image recognition model; determining the original lesion category with the maximum prediction probability value as a target lesion category; obtaining a map weight corresponding to a target lesion category, carrying out classified activation mapping on a characteristic map and the map weight by adopting an activation mapping formula, obtaining a thermodynamic mapping map, superposing the thermodynamic mapping map and a target medical image, generating a target thermodynamic map, and improving an image recognition rate.

The invention discloses a capsule endoscopy auxiliary film reading method, belonging to the biological image recognition technology field and comprising: downloading the image shoot by the capsule endoscopy to the image film reading workstation, before film reading, preprocessing the images by an auxiliary film reading system; in detail, firstly, training a focus site categorizer for preparing to recognize the focus site; secondly, applying the pattern recognition technology to delete the redundant frames with high similarity in the endoscopy image sequence, only retaining the image frame including most information, compressing the endoscope image sequence; finally, matching the compressed image sequence and the focus site categorizer based on the trained focus site categorizer, labeling the focus site therein. The auxiliary film reading system comprises a categorizer train module, a similar frame detection module and a focus characteristic analysis module. The categorizer train module finishes the train of the normal capsule endoscopy image characteristic categorizer and the focus site capsules endoscope image categorizer. The similar frame detection module finishes the image detection with high similarity, removes some redundant image. The focus characteristic analysis module judges whether the focus site is in the image and labels if there is.

The invention discloses a vision field definition enhancement system for gastrointestinal endoscope diagnosis and treatment (ESCGV). A liquid delivery pipe (2) is led out from a washing bottle (1), and passes by a first peristaltic pump (3); and the washing bottle (1) is also provided with a stirring and heating sub-system for stirring and heating the washing liquor therein. The invention also discloses a vision field definition enhancement method for gastrointestinal endoscope diagnosis and treatment. The method comprises the following steps: 1) adding the washing liquor into the washing bottle; 2) magnetically stirring the washing liquor in the washing bottle, and simultaneously heating the washing liquor; 3) setting the temperature of the washing liquor at 25-37 DEG C; and 4) sending the washing liquor into an observation window of a gastrointestinal endoscope. The system and method can obviously enhance the vision field definition under the gastrointestinal endoscope, make the operation convenient and simple, greatly reduce the missed diagnosis and erroneous diagnosis, and obviously increase the treatment quality of the gastrointestinal endoscope.

The invention provides a virtual diagnosis and treatment system. The virtual diagnosis and treatment system comprises a user end and a server end, wherein the server end is used for supporting an online simulation diagnosis and treatment process and comprises a first controller, a first database and a first retrieval module; the user end is used for displaying and controlling the online simulation diagnosis and treatment process and comprises a second controller, an input apparatus, an output apparatus and a virtual 3D generation module; and the first controller of the server end is connected with the second controller of the user end so as to realize data interaction between the server end and the user end. The virtual diagnosis and treatment system brought forward by the invention completely simulates the whole clinic diagnosis and treatment process of a clinic doctor, realizes continuous interaction of the diagnosis and treatment, can provide numerous real cases for users to select, through simulation diagnosis and treatment of the real cases, continuously intensifies diagnosis and treatment logic of the users, widens the knowledge scope of diagnosis and treatment and improves the diagnosis and treatment efficiency.

The invention provides a working method of a medical diagnosis auxiliary system, and the method comprises the following steps: inputting symptom information into a server, and sampling patient examination data; enabling the server to identify keywords and related sentences in the symptom information and extract the corresponding keywords; according to sample training and statistics obtained from historical medical record data, obtaining detailed features of the disease, finding out potential patients, and depicting a disease model; and matching the depicted disease model with a diagnosis modelin a database to obtain a diagnosis model result closest to the symptom information of a patient. The accuracy of data analysis is further improved through continuous data accumulation; compared withInternet inquiry in the prior art, the method improves the accuracy by 70%; the new analysis vocabularies and logic algorithms are continuously expanded according to logic operation of data, the diagnosis capacity of doctors is improved through auxiliary diagnosis, and the misdiagnosis rate and the missed diagnosis rate are reduced. Doctors apply medicine according to symptoms and actual conditions and query results of the method, and the accuracy rate can reach 100%.

A charging method for a vehicle is provided that includes receiving, by a controller, a control pilot (CP) signal from electric vehicle supply equipment (EVSE) and smoothing the CP signal using a duty ratio of the CP signal. In addition, the controller is configured to compensate the smoothed CP signal and determine whether the compensated CP signal is within a predetermined range. The vehicle battery is then charged when the compensated CP signal is within the predetermined range.

The invention provides a single-lead electrocardio abnormal signal identification method based on generative adversarial networks and convolution recurrent neural networks, and mainly solves the problem that data concentration samples are unbalanced. Categories being low in data concentration data volume are subjected to data enhancement, and then identification and classification of electrocardioabnormal signals are performed, so that reference is provided for assistance of doctors, the wrong diagnosis rate and missed diagnosis rate are reduced, and the workload of the doctor can be alleviated. The generative adversarial networks are used for enabling the data concentration samples to achieve the relative balance, so that training the convolution recurrent neural networks can be realized, and good classification effects can be achieved.

A method and device for diagnosing an accelerator pedal signal of an electric vehicle, the method comprising: acquiring the voltage value of the accelerator pedal signal, and judging whether the voltage value of the first accelerator pedal sensor signal and the proportionally converted second accelerator pedal sensor signal voltage value are in the same range. the same preset voltage interval; when the first accelerator pedal sensor signal voltage value and the proportionally converted second accelerator pedal sensor voltage value are in the same preset voltage interval, the absolute value of the difference between the two is obtained and compared with the The maximum allowable error value corresponding to the preset voltage range is compared; when the absolute value is greater than the maximum allowable error value corresponding to the preset voltage range, it is determined that the accelerator pedal signal is faulty. By adopting the method and the device, the accelerator pedal signal of the electric vehicle can be accurately diagnosed, and the hidden dangers of driving safety can be reduced.

The invention relates to the technical field of pelvis and pelvis artery blood vessel tree segmentation, in particular to a deep learning-based pelvis and pelvis artery blood vessel multi-level segmentation method, which is used for solving the problem that an abdomen pelvis and a pelvis artery blood vessel tree cannot be automatically, efficiently and accurately segmented in a multi-resolution CTimage in the prior art. The method comprises the following steps: step 1, preparing and labeling data; 2, preprocessing the data; 3, constructing a first-level segmentation model of the 3D convolutional neural network based on multi-level segmentation; 4, constructing a second-stage segmentation model; 5, training a first-stage segmentation model and a second-stage segmentation model by using thecalibrated data and the synthesized loss function; and 6, performing abdomen information segmentation on the input three-dimensional CT image by using the first-stage segmentation model and the second-stage segmentation model trained in the step 5. According to the method, the abdominal pelvis and the pelvis vascular tree can be automatically, efficiently and accurately segmented in the multi-resolution CT image.

The invention provides a virtual diagnosis and treatment system. The virtual diagnosis and treatment system comprises a user end and a server end, wherein the user end is used for displaying and controlling an online simulation diagnosis and treatment process, and the server end is used for supporting the online simulation diagnosis and treatment process; and the virtual diagnosis and treatment system also comprises a retrieval system, wherein the retrieval system comprises a retrieval module and a database, wherein a part of or all the database is located on the server end. With the virtual diagnosis and treatment system proposed by the invention adopted, the whole process of diagnosis and treatment in outpatient service of clinicians can be completely simulated; continuous interaction of the diagnosis and treatment process can be realized; a large number of real cases can be provided for and selected by a user; based on the simulation diagnosis of the real cases, the diagnosis and treatment logic of the user can be strengthened continuously; the scope of diagnosis and treatment knowledge can be broadened; and diagnosis and treatment efficiency can be improved.

The invention discloses a circuit breaker voiceprint and vibration entropy feature fusion fault diagnosis method. The method comprises the steps of carrying out band-pass filtering on a sound signal,and obtaining the roughness, box dimension, direction degree, contrast ratio, linearity and regularity which reflect time-frequency texture features through generalized S transformation; constructingvoiceprint features based on the roughness, the box dimension, the direction degree, the contrast ratio, the linearity and the regularity which reflect the time-frequency texture features; selecting parameters for a vibration signal, and obtaining permutation entropy through variational mode decomposition; constructing vibration entropy features based on the permutation entropy; performing deep fusion based on the voiceprint features and the vibration entropy features to obtain a joint feature vector; performing learning training of an SVM model on the joint feature vector; and based on the GWO-SVM diagnosis model, optimizing a penalty factor and a mixing coefficient in the SVM model by adopting the GWO model. In the embodiment of the invention, the influence of sensor frequency response and environmental noise on the fault diagnosis of the circuit breaker is reduced.

The invention provides an intelligent assisted diagnosis method for children respiratory diseases and a diagnosis system thereof. The method comprises the steps of S1, acquiring assisted examination information of a to-be-diagnosed child and/or doctor interrogation diagnosis recording information; S2, according to the assisted examination information and/or doctor interrogation diagnosis recordinginformation, screening key information; and S3, according to a pre-established children disease knowledge database system, processing the key information by means of a pre-trained typical symptom diagnosis model, thereby obtaining a diagnosis result. The diagnosis result comprises at least one possible disease, and the characteristic, which corresponds with each possible disease, of the to-be-diagnosed child. The intelligent assisted diagnosis method can help non-expert doctors in improving diagnosis levels for difficult disease, thereby reducing misdiagnosis.

Methods, systems, and computer readable media are provided for processing microscopic images of a biological sample from a patient. One or more images of a blood sample from a microscope is obtained, each image comprising a plurality of different types of cells. The one or more images are processed by a machine learning system to classify individual cells into one of a plurality of cell categories. The cells in each cell category are analyzed to determine characteristics of the respective cell category. A diagnosis or list of possible diagnosis are determined based on the classification and characteristics of the cells for the patient in an automated manner.

The invention discloses a classification method and system based on an imbalanced medical image data set. The method comprises a step of extracting the green channel component of an original medical image, a step of using the histogram equalization to correct an extracted gray image, a step of extracting a texture feature, a wavelet feature and an auxiliary wheel feature from the corrected image, a step of ranking extracted feature samples according to a distance between the samples, a step of dividing uniform feature subsets on the ranked samples, and ensuring the difference between the subsets, a step of using an SVM algorithm and a BP neural network algorithm to train the feature subsets to produce sub classifiers, a step of combining the sub classifiers, and voting to obtain a final classification result. By using the technical scheme of the invention, the negative sample classification accuracy in multi-classification integrated learning is improved significantly, and the high skew of data set sample distribution and the negative sample accuracy in multi-classifier training in the medical field are improved obviously. The reduction of misdiagnosis is helped, and thus the practical value of the classifier is improved.

The invention discloses DDX24 gene mutation and an application thereof. The DDX24 gene SNP (single-nucleotide polymorphism) mutation comprises Glu271Lys, Lys11Glu and Arg436His. The DDX24 gene mutation Glu271Lys, Lys11Glu or Arg436His is remarkably correlated with development of vessels. By means of interference to DDX24, cell migration and vascular formation can be promoted, and occurrence of vascular malformation is reminded. The occurrence condition of the vascular malformation of a human body can be effectively predicted by detecting SNP loci of DDX24, the DDX24 gene mutation can be used for screening genetic defects and is beneficial to true judgment of types of abnormal diseases of the vessels, misdiagnosis is reduced, and specific treatment is convenient.

The invention discloses a human echinococcosis colloidal gold immunochromatographic assay urine testing quick diagnosis test paper card. The test paper card comprises a plastic card box and a detection module in the card box; the plastic card box is provided a sample loading hole and an observation window; the detection module comprises sample filtering paper, a nitric acid fibrous membrane and absorbent paper; echinococcosis resistance fluid antigen humanized single chain variable region antibody which is marked by colloidal gold is absorbed on the absorbent paper and nitric acid fibrous membrane; a quality control line and a detection line are marked on the echinococcosis resistance fluid antigen humanized single chain variable region antibody marked by the colloidal gold on nitric acid fibrous membrane; the sample filtering paper is opposite to the sample loading hole; and the nitric acid fibrous membrane is opposite to the observation window. The test paper card has the advantages of simplicity and convenience, fastness, no invasiveness, reliability and stability.

The invention relates to the technical field of artificial intelligence, and provides a medical consultation auxiliary method and system, a device, and a storage medium. The method includes: registering and acquiring a patient identity and the face information, and pushing the acquired information to a diagnosis department; reacquiring the face information of the diagnosis department, comparing the face information with the pushed information, and making a diagnose through interrogation if the two pieces of information are consistent with each other; making an offline confirmation if not; during the diagnose through interrogation, making a voice record of the communication process between the doctor and the patient, extracting the keywords of the voice record and comparing the keywords with a pre-established disease database, and then outputting a disease report and a medication plan; pushing all the information to a following service window: acquiring and comparing the face information of the service window, executing a service window procedure if consistent, and making an offline confirmation if not. The method can improve the efficiency of medical consultation, improve the accuracy of medication, and reduce disputes between doctors and patients.