81 results about "Disease assessment" patented technology

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 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 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.

The invention discloses a Parkinson's disease assessment method and system based on face video multi-modal physiological feature fusion, and relates to multiple technical fields of computer vision, physiological signal processing and the like, and the method comprises the following steps: S1, based on a continuous face video stream, extracting rPPG signals; then heart rate variability key parameters are calculated, and heart rate variability characteristics are obtained; s2, analyzing the dynamic change of an eye fixation point in the face video based on IPAST, extracting key eye movement behavior parameters, and obtaining eye movement behavior characteristics through a convolution gating loop unit; and S3, inputting the heart rate variability characteristics and the eye movement behavior characteristics into a multi-modal fusion network structure, and outputting a continuous risk score or an illness state label for assisting a doctor in early Parkinson risk assessment. According to the method, multiple physiological signals acquired through videos are deeply integrated, a multi-modal feature collaborative analysis framework is constructed, the subjective limitation of traditional scale evaluation is broken through, and the one-sidedness defect of single biomarker detection is overcome.

The invention relates to the field of agricultural intellectualization, in particular to a crop disease and insect pest real-time identification and analysis system based on deep learning, which comprises an image acquisition unit, a mobile control unit, an image identification unit, a disease evaluation unit and a disease and insect pest prediction unit, the core innovation of the invention lies in that an image recognition unit introduces a Riemannian geometry multi-scale manifold learning framework and comprises a manifold construction module, a manifold feature fusion module and a manifold constraint optimization module, and the manifold construction module maps crop image features to a Riemannian manifold space; the manifold feature fusion module fuses multi-scale feature manifolds through geodesic line connection and parallel transmission; and the manifold constraint optimization module executes network parameter optimization in a Riemannian space. The disease evaluation unit evaluates the severity of the disease based on the identification result; the disease and pest prediction unit predicts the disease development trend based on historical data and environmental information, the disease and pest recognition precision is remarkably improved, and particularly the rare disease recognition capability is improved by 23%.

The invention relates to the technical field of medical data management and analysis, in particular to a lymphedema treatment monitoring data management method and system. Comprising the following steps: synchronously acquiring bioelectrical impedance, tissue thermodynamic characteristics and subcutaneous tissue morphological data of a patient through multi-modal data acquisition equipment; transmitting the data to a cloud processing platform in a distributed manner, and generating a three-dimensional illness state assessment map containing edema quantitative indexes through a machine learning assessment model; generating a personalized treatment strategy adjustment scheme with a confidence score based on dynamic comparison between the current evaluation map and historical data; feeding back the evaluation map and the adjustment scheme to the medical staff through the interaction terminal, and receiving a clinical correction instruction; according to the method, on the premise of strictly protecting patient data privacy, cross-mechanism multi-modal knowledge sharing can be realized, and the contradiction between a data island and a model effect is solved.

Systems and methods for providing assessments of a disease using sensor data for a subject captured during day-to-day living are provided. The sensor data includes accelerometer data and is acquired by a sensor disposed on the subject's wrist while the subject performs both active and sedentary activities. The sensor data is analyzed for one or more bouts of activity; the sensor data is filtered, optionally rotated, and Fourier transformed. Spectral analysis parameters are then calculated from the transformed data to detect disease-relevant movement patterns. Movement distribution parameters for the accelerometer data associated with the disease-relevant movement patterns are calculated and compared to one or more disease scores to evaluate a progression of the disease or an efficacy of a treatment

The invention provides an experimental dyeing result image processing and calculating method. The method comprises the following steps: identifying a dyeing positive region in a preprocessed tissue sample digital image by using a first identification model, and carrying out area quantification on the identified dyeing positive region to obtain positive region area data; using a second recognition model to recognize and extract the intracellular cavitation region in the preprocessed tissue sample digital image, and performing area quantification on the extracted intracellular cavitation region to obtain cavitation region area data; calculating a dyeing positive region area proportion based on the positive region area data, and calculating a cavitation region area proportion based on the cavitation region area data; and inputting the area proportion of the dyeing positive region and the area proportion of the cavitation region into a preset disease evaluation model, and outputting a disease state evaluation result. According to the method, the accuracy and comprehensiveness of experimental dyeing image analysis are improved, and the automation degree and result credibility of pathological analysis can be remarkably improved.

The invention discloses an inflammatory bowel disease activity evaluation system based on multi-modal endoscopic feature fusion, and relates to the field of medical information processing. The system comprises a multi-modal data receiving module used for receiving first EUS microarray multi-modal endoscope dynamic sequence data; the image preprocessing module is used for carrying out noise reduction processing and determining a first white light effective observation image and a first ultrasonic effective observation image; the white light surface feature extraction module is used for extracting mucous membrane surface features by using a white light surface feature extraction model to determine mucous membrane features under a first white light endoscope; the ultrasonic transmural feature extraction module is used for performing structure identification and measurement by using an ultrasonic transmural feature extraction model to determine first ultrasonic lower mucous membrane features; and the multi-modal endoscope IBD activity evaluation module is used for determining the IBD activity based on the multi-modal endoscope characteristics by using the multi-modal dynamic endoscope IBD activity evaluation model. Compared with the prior art, the method has the advantages that the limitation of disease assessment based on single-mode and static pictures in the past is broken through, and the accuracy of IBD activity assessment results is improved.

The invention discloses an intelligent evaluation method for screening multiple diseases of lumbar vertebra based on deep learning, and relates to the technical field of intelligent screening of lumbar vertebra diseases, and the method comprises the steps: collecting multi-source data, and obtaining lumbar vertebra CT, MRI, bone mineral density images, clinical diagnosis and treatment data and operation related parameters; preprocessing data, denoising images, segmenting key structures, and standardizing non-image data; a deep learning model is constructed, image features are extracted through CNN branches, non-image features are processed through Transform branches, and disease classification, severity assessment and risk prediction are achieved in combination with multi-task learning; integrating the multi-modal features through a feature fusion optimization algorithm, and outputting an evaluation result; doctor interaction correction and model iteration are supported, and the generalization ability of the system is improved. According to the method, multi-source data are integrated, the accuracy and efficiency of lumbar vertebra multi-disease assessment are improved, postoperative risk prediction is optimized, clinical diversified requirements are met, and powerful support is provided for precise diagnosis and treatment and rehabilitation.

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.

The invention discloses a wheat stinking smut infection degree detector and method based on an array type gas sensor. The method comprises the following steps: S1, acquiring sensor response data of wheat samples with different infection degrees by using an instrument; s2, measuring the trimethylamine content of the wheat sample, and taking the measured TMA concentration as a reference basis of the disease degree; s3, according to the TMA content distribution condition, dividing the wheat samples into three types, namely healthy, mild infection and severe infection; a sensor response signal is used as model input, the disease level corresponding to the TMA measured value is used as a model label, and a data set is constructed; s4, based on a deep learning framework, establishing a wheat stinking smut infection degree grading model; and S5, collecting gas sensor response data of a to-be-detected wheat sample, inputting the data into the trained model, automatically judging the severity of the disease by the model, and outputting a classification result. The volatile gas characteristics of the wheat sample can be analyzed in real time without destroying the wheat sample, and the disease assessment result is output in combination with the intelligent analysis model.

The invention provides an analysis kit and a detection method of prostate small body leaked protein, and relates to the technical field of kits.The analysis kit comprises a kit body, a heating cavity is formed in the portion, adjacent to the lower portion of a storage groove, in the kit body, and an electric heating piece is fixedly installed on the top face of the inner end of the heating cavity; the microcontroller synchronously starts the failure judgment timing module, if the detection articles are not used within one hour, the module feeds back a signal to enable the microcontroller to control the micro motor to lock the box cover again, and the electric heating sheet in the heating cavity of the box body is started to forcibly discard the reagent, so that the detection error caused by the active failure reagent is prevented; according to the present invention, the accurate detection is ensured through the preparation before the detection and the reagent aging control, and the problems that the accuracy of the detection result is affected due to the lack of the temperature balance control mechanism and the reagent aging control function of the current prostate small body leakage protein detection kit so as to interfere the prostate disease clinical diagnosis and illness state evaluation are solved.

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

To provide a system, a method, and a device for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking.SOLUTION: A system, a device, and a method 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. The system, device, and method are further configured to generate one or more assessments of plaque-based diseases from raw medical images by using one or more of the identified features and / or quantified parameters.SELECTED DRAWING: Figure 2A

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 provides a recent modern historical building disease assessment method which comprises the following steps: 1) determining a historical building assessment part which comprises disease assessment factor grade division and disease assessment factor weight calculation; 2) obtaining disease evaluation indexes; according to the method, the limitation of traditional qualitative analysis is broken through, and quantitative treatment is performed on the diseases, so that the safety performance of the building is reflected more accurately, it is ensured that the historical building is stored for a long time and properly repaired, and the disease state of the whole building or a specific component (such as a single wall or a single column) can be comprehensively reflected.

The invention discloses a post-stroke hemiplegia rehabilitation evaluation method based on an attention map control network, and the method comprises the steps: firstly constructing a hemiplegia illness attention map control network framework HemiplegiaAGCN, carrying out the cleaning and classification of the clinical physical examination evaluation text data of a patient, and carrying out the analysis of the clinical physical examination evaluation text data; a symptom text analysis module and a scale analysis module are respectively designed to process a symptom text and a scale evaluation text, features of the symptom text and the scale evaluation text are analyzed in a targeted manner, then the features are fused by using a selective attention fusion unit designed in an illness state evaluation module, and finally classification evaluation is performed. The hemiplegia condition attention map control network designed by the method processes clinical physical examination evaluation text data of a patient in a targeted manner from different angles, extracts features most related to the condition of the patient and performs feature fusion to execute a condition evaluation classification task, so that the accuracy of hemiplegia condition evaluation is improved, doctors are assisted to perform rehabilitation evaluation, and the diagnosis efficiency is improved. The purposes of improving the efficiency of doctors, reducing the misdiagnosis rate and improving the utilization rate of medical resources are achieved.

The invention belongs to the technical field of biomedical products, and relates to application of immediate early genes Fos and Egr2 in tinnitus disease model evaluation and intervention products, the immediate early genes Fos and / or Egr2 are used as biomarkers for tinnitus maintenance, and tinnitus diseases are evaluated through detection of down-regulation of the immediate early genes Fos and Egr2 in an anterior claspite cortex ACC brain region when tinnitus occurs. Tinnitus is relieved by regulating and controlling early genes Fos and Egr2 in an AC brain region of the auditory cortex; according to the invention, diagnosis markers and treatment application are covered at the same time, a complete tinnitus disease assessment intervention system is formed, the bottleneck that traditional tinnitus research lacks objective biomarkers and targeted treatment means is overcome, and a solution is provided for clinical assessment and intervention of tinnitus through precise intervention at a gene level.

The invention discloses a pediatric sepsis clinical evaluation and diagnosis and treatment method based on a large model. The method includes: acquiring an input patient medical record; the illness state assessment task is decomposed into two stages of index analysis and risk prediction; then, an index classification template and a risk adjustment template are designed for the two stages respectively, an index classification result is used for generating a comprehensive illness condition summary report, and the risk probability is adjusted according to the summary report; according to the method, database retrieval knowledge is selected according to the condition summary, a retrieval result is discriminated through a discrimination model, then a better retrieval result is obtained through a reflection and modification mechanism, and a better treatment suggestion is finally helped to be obtained through multiple rounds of iteration. According to the method provided by the invention, the comprehensiveness of index evaluation, the accuracy of risk prediction and the quality of treatment suggestions are improved, and comprehensive evaluation and early warning and reasonable personalized diagnosis and treatment of the sepsis of children can be considered.

Provided herein are methods for assessing response to inflammatory disease therapy. The methods include performing immunoassays to generate scores based on quantitative data for expression of biomarkers relating to inflammatory biomarkers to assess disease activity in inflammatory diseases, e.g., rheumatoid arthritis. Also provided are methods of adjusting disease activity scores to account for variables that can influence such scores.

The present invention relates to the field of diagnostics. In particular, it relates to a computer-implemented method for assessing a medical condition in a subject comprising the steps of determining the amount of at least one biomarker of interest for said medical condition in a sample of the subject, comparing the determined amount of the at least one biomarker to a reference, and assessing the medical condition in the subject based on the comparison and improving the reliability of the said assessment by integrating at least one clinical parameter value of the medical condition obtained from a dataset of continuously determined clinical parameter values comprising the said at least one clinical parameter value into the biomarker-based assessment, wherein the sample and the at least one clinical parameter value have been obtained from the subject within a short time window, wherein said at least one biomarker is determined continuously by using a point-of-care analytical device and / or wherein the dataset comprising the at least one clinical parameter is determined by a wearable sensing device. Moreover, the invention provides a system and a device for assessing a medical condition in a subject as well as the use of the amount of at least one biomarker of interest for said medical condition in a sample of the subject or a detection agent therefor for assessing a medical condition in said subject.

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.