44 results about "Disease assessment" patented technology

A system and method for automated image analysis which may enhance, for example, capsule endoscopy diagnosis. The system and methods may reduce the time required for diagnosis, and also help improve diagnostic consistency using an interactive feedback tool. Furthermore, the system and methods may be applicable to any procedure where efficient and accurate visual assessment of a large set of images is required.

A method of calculating a disease assessment by analyzing a medical image, comprising (1) extracting at least one lesion feature value from the medical image; (2) extracting at least one risk feature value from the medical image; and (3) determining the disease assessment based on the at least one lesion feature value and the at least one risk feature value. The method employs lesion characterization for characterizing the lesion, and risk assessment based on the lesion's surroundings, i.e., the environment local and distal to the lesion. Computerized methods both characterize mammographic lesions and assess the breast parenchymal pattern on mammograms, resulting in improved characterization of lesions for specific subpopulations, combining the benefits of both techniques.

In a hepatic disease-evaluating apparatus, an indicator calculating unit calculates an index indicating the degree of hepatic fibrosis from amino acid concentration data to be evaluated including amino acid concentration value, based on one or more indices of fractional expression having amino acid concentration as variable. A disease state evaluating unit evaluates the disease state of the hepatic disease to be evaluated, based on the index value.

The embodiments of the invention provide a disease assessment method, a terminal apparatus and a computer readable medium. The method comprises the steps of: acquiring target sleep apnea data in a first unit time, and inputting improved HMM models with the number of a to obtain a sleep apnea assessment result output by a target improved HMM model to indicate whether a user corresponding to the target sleep apnea data has experienced sleep apnea in the first unit time, wherein the target improved HMM model is the improved HMM model with the maximum likelihood in the improved HMM models with thenumber of a, the observation probability matrixes of the improved HMM models are RF models, and the RF models of the different improved HMM models are obtained by inputting and training different first sleep apnea sample data based on a first classification disease level. According to the embodiments, the method can intelligently diagnose the sleep apnea disease for users and improve the accuracy, convenience and rapidness of disease assessment.

The invention discloses a mobile CT (computed tomography) scanner system which is composed of a control console subsystem and an IPC (industrial personal computer) subsystem. The mobile CT scanner system mainly realizes scanning control, data collection and image reconstruction on mobile CT and finally forms high-quality image display to achieve the objectives of medical examination of focus and disease assessment. The control console subsystem comprises a patient information registration module, a scanning control module, a scheme management module, a patient browser module, an image reconstruction module and an image display printing module. The IPC subsystem has functions of patient information registration, scanning control, original data collection, image reconstruction and image data transmission, receives an order of a control console and forwards the order to a main control module in a cross-process manner for execution. The main control module is responsible for receiving the order and parameters sent by the IPC subsystem, analyzing and issuing the order and the parameters to hardware for execution, monitoring state of all hardware, scheduling for normal business processing and abnormality processing and returning the state to the scanning control module.

The invention relates to a muscle disease assessment method and system and an electronic device. The muscle disease assessment method includes the steps: a driving passive motion of a subject by external driving, and acquiring a motion angle of the subject; b acquiring dynamic change of detected skeletal muscle without the motion angle by the aid of an ultrasonic shear wave elasticity imaging technique, and extracting elasticity measurement data of the dynamic change; c extracting a corresponding relationship between elasticity modulus of the detected skeletal muscle and a joint angle from theelasticity measurement data, and taking the corresponding relationship between the elasticity modulus and the joint angle as characteristics to make a data set; d building a probabilistic neural network model, inputting the data set into a probabilistic neural network to perform training, and outputting a muscle disease of the subject and categories of the muscle disease by the probabilistic neural network. According to the method, by the aid of the artificial intelligence neural network, classification results are more reliable, generalization ability is better, and a new method is providedfor assessment of muscle diseases.

An apparatus which can objectively assess and display to what extent and to which disease the subject belongs based on the signals of changes in hemoglobin amount measured by the optical measurement apparatus is provided.

The disease assessment apparatus comprises the feature extraction unit 15 for extracting features of the hemoglobin waveform, the feature analysis unit 16 and the storage unit 12 for storing features by each disease as database. The disease is assessed by extracting various kinds of feature amounts from the hemoglobin signals of the subject measured by the optical measurement apparatus in the feature extraction unit 15 and by applying multivariate analysis of similarities between feature amounts extracted and feature amounts stored by disease in the storage unit 12. Results of assessment are shown as scores expressing similarity with each disease group in a bar graph.

The invention discloses application of miR-155 as a molecular marker in diagnosis of preeclampsia (PE). The nucleotide sequence of miR-155 is shown as SEQ. ID. NO.1. By means of experiments, the invention finds that the expression of miR-155 in sera of PE patients is significantly enhanced compared with normal pregnant women, and the ratio of PE patients' serum miRNA to urine miRNA is significantly increased compared with a normal pregnancy group, and ROC mapping shows that miR-155 has medium diagnostic value on preeclampsia. Therefore, through determination of patients' peripheral circulation miR-155, and combination of the clinicopathological features thereof, the invention provides a reliable index and basis for diagnosis, treatment and disease assessment of preeclampsia.

The invention relates to a knee joint disease assessment system based on Internet-of-things cloud server. The knee joint disease assessment acquires a knee joint motion signal, a thigh motion signal and a shank motion signal of a user through a knee joint data collecting module (101) and transmits to a processing module (102) for performing processing determining and forming knee joint injury information. Through a user management terminal (104), the knee joint motion information and the knee joint injury information are displayed in an interface and transmitted to the cloud server (105) for forming a user first electronic medical record. A medical affair assessment terminal (106) performs assessment on the knee joint state of a user according to information in the user first electronic medical record, thereby forming a user second electronic medical record. The knee joint disease assessment system based on the Internet-of-things cloud server can acquire the knee joint motion information in a motion state anytime and anywhere, thereby determining an injury degree of the knee joint and generating a medical suggestion. Professional testing of the user in a hospital is not required, and high convenience is brought to the user.

The invention relates to the field of medical systems, specifically to an Internet-based gynecological disease medical diagnosis and treatment control system, which comprises a patient terminal, a doctor terminal and a server which communicate with one another through the Internet, wherein a registration and login module, a patient symptom acquisition module, an examination item recommendation module, an examination item reservation module and a doctor-seeing record registration module are loaded in the patient terminal, and a registration login module, a patient parameter acquisition module,a patient disease evaluation module and a patient disease diagnosis and treatment module are loaded in the doctor terminal. According to the invention, intelligent pre-examination of the patient is realized in a questionnaire manner, and recommendation of detection items is realized based on a BP neural network model, so that the workload of doctors can be reduced, the time spent by the patient ina hospital can be greatly shortened, and tracking and return visit of symptoms of the patient after treatment are realized at the same time.

The invention discloses a disease assessment table generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a target disease type in a target medical record template, and calling at least one target diagnosis and treatment factor corresponding to the target disease type from a pre-established target database; according to the access pathcorresponding to the at least one target diagnosis and treatment factor, respectively calling a target diagnosis and treatment parameter of each target diagnosis and treatment factor; and importing each target diagnosis and treatment parameter into a content editing control corresponding to the corresponding target diagnosis and treatment factor, and generating a medical record assessment table corresponding to the target disease type. Through the technical scheme of the embodiment of the invention, the medical record evaluation table is flexibly configured, and the working efficiency of doctors is greatly improved.

The invention discloses a method for extracting related posterior cingutate function connectivity characteristics of a type 2 diabetes patient with cognitive impairment. The method comprises the following specific steps: sample personnel collection, medical history and biological characteristic measurement, cognitive assessment, MRI data collection, small vessel disease assessment, functional dataanalysis, statistical analysis, head movement analysis, functional connection analysis and correlation analysis. According to the invention, the weak FC of the PCC to a plurality of brain regions ismore accurately verified to be related to the cognitive impairment of a T2DM patient; and the FC pathways of the PCC may be used as biomarkers to further reveal a biological mechanism in the potentialneuropathological process of T2DM-related cognitive impairment, and may provide evidence for further molecular biological research.

The invention relates to a gestational disease evaluation system. Compared with the prior art, the gestational disease evaluation system comprises a sample acquisition module for acquiring genital tract secretion of a pregnant woman and preparing a secretion sample; a first detection module used for detecting the bacterial flora metabolism value of the sample; a sample screening module used for comparing the flora metabolism value of the sample with the reproductive tract flora metabolism value of a pre-stored standard population sample in a database and confirming the sample with a large comparison value difference; a second detection module used for performing microbial gene sequencing on samples with large contrast value differences; a sample processing module used for obtaining the flora structure characteristics of the sample and determining the number and the variety of microorganisms in the flora; a risk assessment module used for disease risk assessment; and an output execution module connected with the risk assessment module and used for providing a personalized treatment scheme. According to the gestational disease evaluation system provided by the invention, early prediction of occurrence of gestational diseases can be realized, intervention can be carried out, and the efficiency of guiding clinical treatment is improved.

The invention belongs to the technical field of cardiovascular and cerebrovascular disease information detection, and discloses a cardiovascular and cerebrovascular disease information detecting system and method. The cardiovascular and cerebrovascular disease information detecting system comprises a patient information collecting module, a physiological data collecting module, a main control module, a data analysis module, a case generation module, a risk warning module, a collected data storage module and a display module. According to the cardiovascular and cerebrovascular disease information detecting system and method, the risk that an individual possibly has a certain disease or certain diseases in a certain period of time in the future can be accurately predicted by the collected data analysis module, then the lifestyle questionnaire survey and routine physical examination are combined, a dangerous state of the individual body at present can be determined, and finally, effectivepreventive interventions are carried out to reduce and avoid the risk of disease occurrence; and meanwhile, timely warning and monitoring of risk factors for high-risk groups of cardiovascular and cerebrovascular diseases are realized through the risk warning module, and the accuracy, timeliness and specificity of cardiovascular and cerebrovascular disease assessment are realized.

A method of evaluating fatty liver related disease includes (I) an obtaining step of obtaining amino acid concentration data on a concentration value of an amino acid in blood collected from a subject to be evaluated and (II) a concentration value criterion evaluating step of evaluating a state of a fatty liver related disease including at least one of fatty liver, NAFLD, and NASH in the subject, based on the amino acid concentration data of the subject obtained at the obtaining step.

The invention discloses a disease risk assessment method and device and a storage medium. The disease risk assessment method comprises the following steps: receiving disease types, to-be-detected data of disease influence factors and to-be-detected gene locus data; according to a preset assignment relation table, assigning values to the to-be-detected data and the to-be-detected gene locus data to obtain corresponding to-be-detected scores, wherein the preset assignment relation table comprises the data range of the disease influence factors, the gene locus data and matching information of the to-be-detected scores; a target disease assessment model is determined according to the disease type and a preset model database, the preset model database comprises matching information of the disease type and the target disease assessment model, and the target disease assessment model is obtained by training the preset disease assessment model through multiple pieces of training sample data; and inputting the to-be-detected score into the target disease assessment model to obtain a disease assessment result. According to the method, the gene loci and the disease influence factors are comprehensively considered for disease assessment, so that the accuracy of disease assessment is improved.

The invention provides a wheat basal stem rot resistance identification method, and relates to the technical field of disease resistance detection. According to the identification method, the spore suspension is injected into wheat seedling stems growing in a greenhouse for FCR inoculation, and phenotype identification can be carried out 15 days after injection (or 22 days after planting) by determining the optimal inoculation concentration and injection time point. According to the identification method disclosed by the invention, the FCR is inoculated by directly injecting the spore suspension into the stem part of the wheat seedling for the first time, and the disease assessment can be completed within 22 days according to the inoculation concentration and the injection time point. According to the identification method, the screening efficiency can be remarkably improved, and large-scale germplasm screening and gene function research are facilitated.

The invention relates to a method for extracting and processing MMN signal features for evaluating a sensory gating function. The method comprises the following steps: collecting brain wave data; performing MMN preprocessing; performing umerical statistical analysis on the amplitude and the incubation period of the MMN by adopting a feature analysis method; performing machine learning classification on the time-dependent potential to obtain a classification result; comparing the event-related potential waveforms before and after the treatment of the patient with the three groups of average levels to obtain an analysis result; and extracting the MMN amplitude and the incubation period. The invention further relates to a device and a processor for extracting and processing the MMN signal features for evaluating the sensory gating function and a storage medium thereof. According to the method, the device and the processor for extracting and processing the MMN signal characteristics for evaluating the sensory gating function and the computer readable storage medium thereof, the waveform and the amplitude of a tested MMN curve and the waveform and the amplitude of a standard curve are compared, and the sensory gating function is evaluated; and an MMN sensory gating system is used to predict mental diseases and evaluate the treatment effect of the patient through machine learning.

A disease assessment method includes using a machine learning system and 2-D image data to quantify an extent of disease in the patient, wherein the machine learning system has been previously trained using 3-D image data for each of a plurality of 3-D images obtained from a plurality of subjects, and 2-D image data for each of a plurality of 2-D images obtained from the plurality of subjects, wherein each of the 2-D images is associated with a corresponding one of the 3-D images and is captured at the same time as the corresponding one of the 3-D images or within a certain predetermined time frame of capturing the corresponding one of the 3-D images, and wherein a ground truth for the 2-D images comprises for each of the 3-D images a quantification of an extent of the predetermined disease based on the 3-D image data for the 3-D image.