48 results about "Disease assessment" patented technology

The invention discloses a bridge structure low-altitude inspection and disease assessment system and method based on deep learning, and belongs to the technical field of bridge disease detection and structure health monitoring. The system comprises a multispectral adaptive image acquisition module, a multi-scale disease detection and feature extraction module, a digital twin mapping and disease positioning module and a disease evolution prediction and maintenance decision module. The system dynamically adjusts acquisition parameters according to environmental conditions, accurately identifies diseases of different scales based on a multi-scale convolutional neural network, realizes centimeter-level accurate positioning of the diseases through a three-dimensional digital twinborn model, analyzes a disease time sequence evolution trend and generates graded maintenance suggestions, and realizes adaptive optimization through a closed-loop feedback mechanism. The method is high in environmental adaptability, high in detection precision and accurate in positioning, has disease evolution analysis capability, and provides comprehensive technical support for bridge health monitoring and intelligent management.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

This invention discloses an image recognition-based method for assessing grape powdery mildew. By introducing standardized sample preparation and uniform inoculation methods, this invention reduces the interference of randomness and spatial heterogeneity of natural field infection on the assessment results, making the disease occurrence process more controllable among different materials, thereby improving the stability and comparability of phenotypic data. Simultaneously, image recognition and machine learning technologies are used to automatically identify and calculate the area of ​​powdery mildew-infected regions, avoiding the reliance on the experience of assessors in traditional manual visual grading. This enables a continuous quantitative description of the degree of powdery mildew infection, improving the consistency and accuracy of the assessment results. This invention constructs a large-scale image analysis workflow that can be used for batch processing, significantly improving the efficiency of disease assessment, reducing the workload of manual investigation, and making this method applicable to applications requiring the processing of large numbers of samples, such as germplasm resource evaluation and breeding material screening.

The invention relates to the technical field of biological medicines, in particular to a polypeptide for evaluating the severity of SARS-CoV-2 infection. The polypeptide is SGLKTILRKGGRTIA (SEQ ID NO: 1), when the polypeptide disclosed by the invention is used for diagnosing SARS-CoV-2 infection and evaluating the severity of the SARS-CoV-2 infection, relatively high sensitivity and specificity are achieved, and an important basis is provided for clinical diagnosis and illness state evaluation.

The invention discloses a lung disease diagnosis and grading method based on quantitative CT image omics and deep learning. According to the method, through multi-dimensional information integration and model optimization, two core tasks of disease judgment and disease condition grading can be completed at the same time, and comprehensive support is provided for clinical diagnosis and treatment. The model sets feature priorities by referring to clinical diagnosis logic in the training process, the judgment result and the grading standard are completely matched with clinical general specifications, and the model can be directly applied to diagnosis and treatment decision-making without secondary conversion of doctors. After multi-center clinical verification and iterative optimization, the stability and the accuracy of the model are fully guaranteed, subjective errors caused by manual film reading can be effectively reduced, the diagnosis efficiency can be improved, the chronic obstructive pulmonary disease screening capability of primary medical institutions can be remarkably improved, early diagnosis and early treatment of more patients can be helped, and the clinical application prospect is wide. Therefore, the morbidity and disability rate of diseases are reduced, a clinical management path is optimized, and the overall disease burden is relieved.

To provide a AI inquiry device, a AI inquiry method, and a program capable of reducing a burden on a patient during AI inquiry.SOLUTION: The AI inquiry device includes an image inputting part, a disease candidate estimating part, and a disease estimating part. The image input unit receives an input of a face image of a patient. The disease candidate estimation unit extracts symptoms of the patient from the face image of the patient to estimate disease candidates of the patient based on the symptoms of the patient by using the image recognizing AI model. The disease estimation unit uses the interactive AI model to output a question to the subject, receive an input of a response of the subject to the question, and estimate a disease of the subject from candidates for the disease of the subject based on the response of the subject.SELECTED DRAWING: Figure 2

This invention discloses a system for assessing the activity of inflammatory bowel disease (IBD) based on multimodal endoscopic feature fusion, relating to the field of medical information processing. The system includes: a multimodal data receiving module for receiving dynamic sequence data from a first EUS microarray multimodal endoscopy; an image preprocessing module for noise reduction and determining first effective white light observation images and first effective ultrasound observation images; a white light surface feature extraction module for extracting mucosal surface features using a white light surface feature extraction model to determine the first white light endoscopic mucosal features; an ultrasound transmural feature extraction module for structural identification and measurement using an ultrasound transmural feature extraction model to determine the first ultrasound-guided mucosal features; and a multimodal endoscopic IBD activity assessment module for determining IBD activity based on multimodal endoscopic features using a multimodal dynamic endoscopic IBD activity assessment model. Compared to existing technologies, this invention overcomes the limitations of previous disease assessments based on single-modality, static images, improving the accuracy of IBD activity assessment results.

The invention relates to the technical field of physiological signal analysis, in particular to a neuropediatric disease assessment system for real-time physiological signals, which comprises a resting feature extraction module, a mark generation module, an abnormality judgment module, an abnormal fragment division module and a disease fluctuation labeling module. According to the method, by collecting the physiological signals of the child patient in the specific period, extracting the amplitude peak value of the physiological signals in the resting state and constructing the dynamic amplitude feature reference, the fluctuation range of the individual physiological baseline of the child patient can be more accurately captured, and compared with simple statistical feature matching, the method has the advantage that the accuracy is higher. According to the method, the adaptive capacity to individual differences and physiological natural variability is improved, real-time changes of physiological signals are continuously monitored, whether the multi-channel signals continuously deviate from respective reference intervals or not is judged, continuous physiological abnormal events instead of transient accidental artifacts are effectively recognized, then the signal strength in the deviation time periods is deeply analyzed, and the accuracy of the physiological abnormal events is improved. And significant anomalies in intensity can be accurately identified.

The application discloses an internet hospital hierarchical diagnosis and treatment and resource scheduling method and system, the method comprises the following steps: in response to the diagnosis and treatment request sent by the user, obtaining the diagnosis and treatment data containing the patient's complaint information; inputting the diagnosis and treatment data into the disease assessment model for feature extraction, and outputting a feature vector representing the patient's disease state; the feature vector at least includes a first dimension parameter representing the emergency degree of the disease and a second dimension parameter representing the complexity of the disease; based on the feature vector, determine the target resource node in the hierarchical medical resource database; according to the target resource node, generate a resource scheduling instruction, and establish a communication link between the user terminal and the corresponding server of the target resource node. The application introduces a two-dimensional evaluation system of emergency degree and complexity, combines a dynamic threshold adjustment mechanism based on supply and demand prediction, and significantly improves the utilization efficiency of medical resources and the accuracy of diagnosis matching.

The invention provides a disease assessment system and method, an electronic device and a medium wherein a data acquisition module is used for acquiring form data and clinical text record data of a target patient; the large model reasoning module is connected with the data acquisition module, and the large model reasoning module is used for performing semantic understanding and reasoning on the clinical text record data based on the medical knowledge base and outputting at least one intermediate semantic understanding result; the rule engine module is connected with the data acquisition module and the large model reasoning module, and the rule engine module is used for receiving the form data and the at least one intermediate semantic understanding result and performing combinational logic operation on the at least one intermediate semantic understanding result according to a predefined evaluation rule; and the assessment result generation module is used for generating a final disease risk assessment conclusion of the target patient according to the logical operation result of the rule engine module. According to the invention, automatic and accurate disease risk assessment is realized, and the clinical decision-making efficiency and quality are improved.

The invention belongs to the technical field of disease monitoring, and discloses a remote-sensing-based urban edge road disease monitoring and evaluation method and a remote-sensing-based urban edge road disease monitoring and evaluation system. Comprising the steps of collecting remote sensing image data and geographic information in real time, and executing data cleaning to obtain an original road assessment data set; performing deformation evaluation, performing semantic analysis on a deformation evaluation result, and outputting a disease tendency label; performing weight adjustment in combination with a disease tendency label, and outputting a score fusion proportion; extracting disease features based on the original road assessment data set, and calculating a disease score in combination with a score fusion proportion; performing trend identification based on the disease score, and outputting a trend field; combining the trend field and the disease score to carry out space aggregation on the corresponding road section to obtain an effective disease area; performing visualization processing on the effective disease area to obtain disease assessment visual data, and sending the disease assessment visual data to a preset monitoring terminal; the semantic intelligibility and the score adaptability of urban edge road disease monitoring evaluation are improved.

The invention belongs to the technical field of biological medicine, and particularly relates to a biomarker of prurigo nodularis and application of the biomarker. The invention discloses that protease activated receptor 2 (PAR2) regulates expression of Th2 type cytokines through a G alpha12-RhoA-ERK signal channel for the first time, and further participates in morbidity of prurigo nodularis (PN). According to the embodiment of the invention, various experimental means such as qRT-PCR, Western Blot, IHC, IF and the like are utilized to verify expression up-regulation of PAR2 and related molecules in skin tissues of a PN patient and the correlation of the PAR2 and related molecules with clinical symptoms. Molecules such as PAR2, RhoA, ERK, IL-4, IL-31 and the like are proved to be highly related to pathological characteristics of PN. The molecules can be used as diagnostic markers or target spots for treating prurigo nodularis, and can be used for early diagnosis of PN, disease assessment or drug development.

The application discloses a bridge structure low-altitude inspection and disease assessment system and method based on deep learning, and belongs to the technical field of bridge disease detection and structure health monitoring. The system comprises a multispectral adaptive image acquisition module, a multiscale disease detection and feature extraction module, a digital twin mapping and disease positioning module, and a disease evolution prediction and maintenance decision module. The system dynamically adjusts the acquisition parameters according to the environmental conditions, accurately identifies different scale diseases based on a multiscale convolutional neural network, realizes cm-level accurate positioning of the diseases through a three-dimensional digital twin model, analyzes the time sequence evolution trend of the diseases and generates grading maintenance suggestions, and realizes adaptive optimization through a closed-loop feedback mechanism. The application has strong environmental adaptability, high detection precision, accurate positioning, and disease evolution analysis capability, and provides omnibearing technical support for bridge health monitoring and intelligent management.

The invention discloses an unmanned aerial vehicle and detection vehicle cooperative monitoring system and method for highway infrastructures, and belongs to the cross technical field of image data processing and material physical performance testing. The method comprises the steps that road surface state scanning data collected by a detection vehicle in real time is obtained, and suspected key disease features are recognized; generating a ground trigger signal containing the disease position coordinate and the disease type; sending the ground trigger signal to an unmanned aerial vehicle through a preset low-delay communication link; performing dynamic adjustment on the basis of the ground trigger signal, generating an unmanned aerial vehicle fine review index, and obtaining aerial multi-dimensional review data of a target area; and carrying out association fusion on the pavement state scanning data and the aerial multi-dimensional recheck data to generate a comprehensive disease assessment result. A dynamic closed-loop cooperation mechanism based on real-time state perception and multi-source data fusion under a unified space-time reference are adopted, so that intelligent linkage of space-ground resources, multi-dimensional accurate diagnosis of diseases and self-adaptive efficiency optimization can be realized.

This invention discloses a Parkinson's disease assessment method and detection wristband based on aerial spiral drawing, relating to the field of Parkinson's disease screening technology. The key technical points include the following steps: outputting pre-set assessment task guidance information to the user to guide the user in completing pre-set action sequences for initial calibration, static holding tasks, and aerial drawing tasks; collecting raw motion signals of the user's hand while the user executes the pre-set action sequences according to the guidance information, and recording the start and end time markers for each task; segmenting the time-series motion data sequence according to the start and end time markers to obtain task motion data sub-sequences corresponding to the pre-set assessment tasks; inputting the task motion data sub-sequences into a pre-set assessment model to obtain a motion abnormality risk score; and generating a Parkinson's disease risk assessment result based on the motion abnormality risk score. The effect is to provide data support for monitoring the course of Parkinson's disease and evaluating treatment efficacy.

The invention provides a recurrence risk assessment method for treating osteomyelitis through an induced membrane technology, and belongs to the technical field of disease assessment. The method comprises the steps of S1, multi-center data and queue design; s2, two high-annuity radiologists sketch ICCgt layer by layer by adopting a blind method; if 0.80, the ROI is locked; a difference gt; when 0.80, third-person arbitration is introduced; s3, high-dimensional feature extraction and refining; s4, machine learning modeling is carried out; and S5, interpretable output is carried out. According to the method, the scoring objectivity and consistency are remarkably improved; the change of the internal microstructure of the induction film is quantified in vivo and noninvasively for the first time; individualized operation opportunity adjustment can be achieved through longitudinal monitoring, and a result can be explained and can directly guide treatment decisions.

The application provides a shield segment disease evaluation method based on a state space equation and a physical information neural network, and comprises the following steps: S1, on the basis of shield segment structure state space analysis theory, introducing disease influencing factors, establishing a shield segment structure state space equation containing diseases, and using the equation to establish a sample database of segment responses containing diseases; S2, based on the control equation, node equation and boundary constraint equation in the state space equation, constructing a shield segment disease identification physical information neural network and training the same; S3, after the training of the physical information neural network is completed, identifying the disease segment and its degree according to limited segment displacement monitoring information. The method has the dual advantages of analytical solution and machine learning algorithm, and since the core is to solve the state space equation derived based on the energy equation, the physical variables have strong interpretability, and the output result also conforms to the physical law.

The embodiment of the invention discloses a service bridge intelligent monitoring and maintenance method and system, and the method comprises the steps: collecting original bridge state data in real time, and carrying out the preprocessing of the original bridge state data, and obtaining the current bridge state data; based on the current bridge image data, performing disease evaluation by using a convolutional neural network to obtain an initial disease recognition result; verifying the initial disease recognition result based on the current bridge dynamic response data to obtain an updated disease recognition result; generating a bridge performance degradation trend through a multi-scale degradation model based on the updated disease recognition result, the bridge design parameters, the historical maintenance record of the bridge and the current environment coupling parameters; calculating a bridge network connectivity probability through a full-connection neural network based on the bridge performance degradation trend; generating a maintenance budget allocation scheme of the service bridge based on the historical bridge state data, the current bridge state data and the bridge network connection probability; and maintaining the service bridge based on the maintenance budget allocation scheme.

The invention relates to the field of medical risk prediction, in particular to a scleroderma early diagnosis method based on vasculopathy and a related device. Acquiring vasculopathy parameters of the subject, wherein the vasculopathy parameters comprise a nail fold microcirculation total score, a metacarpophalangeal artery systolic period peak flow rate, an ischemia reperfusion blood flow value and a two-hand grip value; s2, substituting the vasculopathy parameters in S1 into a scleroderma risk calculation formula to obtain a risk probability value; the risk probability value is compared with a preset threshold value, and when the risk probability value exceeds the preset threshold value, it is judged that the subject is a high-risk patient suffering from scleroderma; according to the method, the vascular lesion total score obtained after multiple dimensions are integrated can quantify the onset risk of the patient, disease diagnosis is facilitated, and more accurate quantitative indexes are provided for disease assessment and diagnosis and treatment methods.

The invention relates to the technical field of biological detection, in particular to a nano chelate labeled KIM-1 time-resolved immunofluorescence detection reagent and a nano chelate labeled KIM-1 time-resolved immunofluorescence detection method, the detection reagent comprises a solid-phase antibody, a EuNPs (at) PAA (at) SiO2-OH nano chelate coupled KIM-1 antibody, 0.05-20ng / mL series KIM-1 standard substance solution and an auxiliary reagent; the detection method is based on a time-resolved immunofluorescence technology, a KIM-1 antigen is captured through a double-antibody sandwich reaction, the fluorescence value is detected and the concentration is calculated after washing and fluorescence enhancement, the nano chelate bears more Eu < 3 + > through a high specific surface area and amplifies a signal in cooperation with a fluorescence enhancement liquid, so that the detection sensitivity is improved by 5 times compared with that of traditional TRFIA, KIM-1 as low as 0.01 ng / mL can be accurately detected, and the detection method is suitable for industrial production. The kit can rapidly quantify the concentration of KIM-1 in serum or urine, is suitable for early diagnosis and disease evaluation of chronic kidney diseases, diabetic nephropathy, lupus nephritis and the like, and has good result consistency and remarkable clinical application value.

The application provides an experimental staining result image processing calculation method. The method comprises the following steps: using a first recognition model to recognize a staining positive area in a pretreated tissue sample digital image, and performing area quantification on the recognized staining positive area to obtain positive area data; using a second recognition model to recognize and extract an intracellular vacuole area in the pretreated tissue sample digital image, and performing area quantification on the extracted intracellular vacuole area to obtain vacuole area data; calculating a staining positive area proportion based on the positive area data, and calculating a vacuole area proportion based on the vacuole area data; and inputting the staining positive area proportion and the vacuole area proportion into a preset disease evaluation model to output a disease state evaluation result. The application improves the accuracy and comprehensiveness of experimental staining image analysis, and can significantly improve the automation degree and result reliability of pathological analysis.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention discloses an inspection result management device and method and a computer program product, and is applied to the technical field of medical detection. A memory; the processor is used for controlling the man-machine interaction device to display a first display interface, and the first display interface comprises at least one disease evaluation / screening control; wherein each disease evaluation / screening control has a mapping relationship with at least two test indexes in the plurality of test indexes; in response to an operation on the disease evaluation / screening control, obtaining each inspection index having a mapping relationship with the operated disease evaluation / screening control and an inspection result corresponding to each inspection index from a memory; and controlling the man-machine interaction device to display the inspection result of each inspection index obtained from the memory. Since the disease evaluation / screening control has the mapping relationship with the at least two inspection indexes, the screening efficiency of the user is improved.

The invention discloses a biomarker for ulcerative colitis and colorectal cancer related to the ulcerative colitis and application of the biomarker, and belongs to the field of molecular biological technology and medicine. The invention discloses an effect of E3 ubiquitin ligase TRIM50 in ulcerative colitis and colorectal cancer related to the ulcerative colitis. The key regulation effect of TRIM50 in UC and UC-CRC is found and verified for the first time, and the potential of TRIM50 as a novel auxiliary detection biomarker and a therapeutic target is defined. The expression level of TRIM50 can be used as a specific biomarker of UC and UC-CRC, and is applied to early screening of diseases, disease assessment or prognosis; targeted regulation of the expression or activity of TRIM50 is expected to become a new strategy for inhibiting the malignant progress of UC-CRC, and a solid theoretical basis and a screening direction are provided for developing innovative drugs.

This invention relates to the field of pest and disease early warning technology, and discloses a method and system for early warning of seed industry pests and diseases based on big data. The method includes acquiring environmental sound data and leaf image data; extracting lesions from the leaf image data to obtain lesion morphological features; extracting features from the environmental sound data to obtain pest sound features; matching the lesion morphological features and pest sound features with a pre-stored pest and disease database to obtain pest type and disease type; calculating the matching degree between the pest type and the disease type to obtain the pest-disease matching degree; and assessing the damage based on the pest-disease matching degree and a preset matching degree threshold to obtain the disease assessment result. This method has the following advantages: it can improve the accuracy of early warning of seed industry pests and diseases.

The invention relates to the technical field of medical biological detection, provides a novel application of HBV RNA, particularly relates to an application in preparation of an LLV screening kit, and further provides a kit and a detection method for performing LLV screening by quantitative detection of HBV RNA. HBV RNA can reflect cccDNA in the liver of a chronic hepatitis B patient and the level of transcriptional activity of cccDNA. In disease assessment and follow-up visit of chronic hepatitis B patients, as a non-invasive assessment means, serum HBV RNA level dynamic monitoring is helpful for early recognition of potential LLV patients, so that the patients are intervened as soon as possible and disease progression is prevented in clinical diagnosis and treatment of chronic hepatitis B.

The invention discloses an in-vitro sample detection data-based decompensated liver cirrhosis risk assessment method and a related device, and relates to the technical field of liver cirrhosis condition assessment. The method comprises the following steps: acquiring in-vitro detection sample data corresponding to a to-be-assessed blood sample, the in-vitro detection sample data comprises sCD146, prothrombin time, serum albumin and platelet count; performing data preprocessing on the in-vitro detection sample data, namely generating a serum albumin grade variable ALBgrade based on a preset grading threshold value according to the numerical value of the serum albumin; inputting the processed feature data into a pre-trained binary Logistic regression model, and outputting a decompensated liver cirrhosis risk value corresponding to the to-be-evaluated blood sample; the decompensated liver cirrhosis risk value is a prediction probability P output by the binary Logistic regression model. The method can effectively improve the evaluation accuracy of decompensated liver cirrhosis.

This invention discloses a quantitative analysis system for airway changes in chronic obstructive pulmonary disease (COPD) based on three-dimensional reconstruction, belonging to the field of medical image processing and computer-aided diagnostic technology. The system includes an image data acquisition module, an airway tree reconstruction module, a morphological analysis module, a small airway function assessment module, a disease progression prediction module, and a result output module. It ultimately generates a comprehensive report containing three-dimensional visualized images, morphological parameters, small airway function assessment results, and disease progression prediction results. This system achieves precise reconstruction of the entire lung airway down to the 6th and 7th grade bronchi, high-precision measurement of airway morphological parameters, indirect assessment of small airway function via single inspiratory CT, and intelligent prediction of disease progression, providing comprehensive imaging support for early diagnosis, disease assessment, individualized treatment, and prognosis of COPD.

This invention relates to the field of disease assessment technology, providing a method and system for disease assessment based on clinical indicators. The method includes assessing disease based on routine clinical vital signs parameters, defining normal, intermediate, and abnormal ranges for each parameter at the corresponding physiological stage based on the patient's age, establishing an abnormality judgment benchmark suitable for the individual, and avoiding the adaptation bias of general standards. Time-series data is decomposed into continuous, equal-duration minimum fluctuation units. Fluctuation characteristics are extracted and abnormal trigger units falling into the intermediate range are marked, accurately capturing early disease anomalies and overcoming the lag limitations of traditional monitoring. Abnormal trigger units are chained together according to time rules to form a transmission relationship chain. The time lag difference is calculated and linked to treatment plans to generate an emergency treatment library, reconstructing the disease development path and establishing standardized treatment comparison criteria. Real-time monitoring enables rapid alarms for critical situations, and early risk classification warnings and precise matching of treatment plans for early anomalies, improving the timeliness of disease assessment.

The present application relates to medical informatics and clinical auxiliary diagnosis technical field, especially in kind of based on similarity's chronic obstructive pulmonary disease auxiliary analysis method and system, method includes obtaining the clinical diagnosis and treatment data of COPD patient to be analyzed, clinical diagnosis and treatment data at least includes lung function index, clinical symptom score and medical history information;Clinical diagnosis and treatment data are processed to the characteristics, and the disease characteristic vector is constructed;The disease characteristic vector is calculated with the historical COPD patient characteristic library, and the similar COPD patient set is filtered;Based on the clinical outcome information of similar COPD patient set, COPD auxiliary diagnosis analysis result is generated, and is used to assist doctors to carry out disease assessment and risk prompt.The present application can comprehensively reflect the individual difference of COPD, improve the individualization level and clinical interpretability of disease assessment, and be applicable to the outpatient, hospitalization and follow-up management scene of COPD.