147 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.

A method based on a multi-task learning model comprises the following steps: acquiring and processing histopathological image data of gastritis through a data preparation and preprocessing step; feature extraction is performed by using a self-supervised learning pre-trained model, and image blocks are coded into high-dimensional feature vectors; and constructing a multi-task learning model, learning feature representation through a full connection layer module and an attention layer module, and outputting a classification result of each task. And carrying out model training and optimization by using an optimizer, adding multitask loss through a loss function, and dynamically adjusting the model performance. The trained model can automatically detect and grade gastritis, atrophy, acute activity, intestinal metaplasia and other pathological indexes, outputs a standardized evaluation result, and assists in pathological diagnosis. According to the method, a multi-task deep learning framework based on self-supervised learning pre-training is constructed, a traditional single-task modeling mode is broken through, deep learning framework design is driven through pathological index association, and accuracy and clinical practicability of non-neoplastic disease assessment of the stomach are remarkably improved.

The invention discloses an effect evaluation method for a non-dialysis diabetic kidney disease, which comprises the following steps: monitoring the change trend of data by collecting renal function related data of a patient, and generating a basic data set; a dynamic nonlinear weighted regression algorithm is adopted to analyze the multi-dimensional renal function data, data weights are dynamically adjusted, and influence weights of different time periods are distributed according to the change mode of the illness state; the data influence is adjusted in real time through a time correlation mechanism, and a disease assessment curve is generated; according to the method, historical data and real-time data are combined, a patient condition trend line and a prediction result are generated, personalized treatment feedback is output, and intervention suggestions are provided for clinicians based on long-term prognosis prediction. The method is suitable for disease monitoring and treatment feedback of patients with non-dialysis diabetic kidney diseases.

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 diabetic foot assessment method based on wound image recognition. The method comprises the following steps: processing a foot infrared thermal imaging image to generate a temperature anomaly graph; fusing the foot wound image and the temperature anomaly graph to generate a fused image; constructing a foot blood flow analysis model, extracting a blood flow influence factor, and correcting the fused image through the blood flow influence factor; and constructing a joint assessment model, taking the corrected fusion image and historical data of the diabetic foot patient as input of the joint assessment model, predicting a risk assessment level of the diabetic foot, and outputting a disease assessment result of the diabetic foot patient according to the risk assessment level. According to the method, multi-modal medical image fusion analysis is realized, the accuracy of wound area positioning and pathological state expression is improved, the accuracy of lesion area recognition is improved, the illness worsening risk can be predicted in advance, clinical decision can be assisted, the risk of serious complications such as amputation of a patient is reduced, and the prognosis and life quality of the patient are improved.

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 relates to the technical field of disease and insect pest early warning, and discloses a big data-based seed industry disease and insect pest early warning method and system, and the method comprises the steps: obtaining environment sound data and leaf image data; performing scab extraction according to the leaf image data to obtain scab morphological characteristics; performing feature extraction according to the environment sound data to obtain pest sound features; matching according to the scab morphological characteristics and the pest sound characteristics in combination with a pre-stored pest disease database to obtain a pest type and a disease type; performing matching degree calculation according to the pest type and the disease type to obtain a pest and disease matching degree; and carrying out damage assessment according to the pest disease matching degree and a preset matching degree threshold value to obtain a disease assessment result. The method has the following effect that the accuracy of early warning of plant diseases and insect pests in the seed industry can be improved.

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 invention discloses a tumor disease assessment method based on big data, and particularly relates to the technical field of tumor disease assessment. Medical data and living habit data of a patient are collected through multiple data interfaces, integration is carried out through preprocessing and a multi-modal data fusion technology, tumor microenvironment features and drug metabolism features are extracted, a comprehensive feature vector is generated, the effectiveness of a chemotherapy regimen is predicted by using a machine learning model, and when the effectiveness of the chemotherapy regimen is low, the chemotherapy regimen is identified. Newest clinical tests and drug data are introduced regularly, model parameters are dynamically optimized in combination with the chemotherapy regimen improvement effect, it is ensured that the model reflects the newest clinical progress in real time, the problems of model updating lag and drug innovation mismatching are effectively solved, the accuracy and effectiveness of a treatment regimen are improved, and the treatment cost is reduced. And meanwhile, side effects caused by blind attempts are reduced, and powerful support is provided for personalized precise medical treatment.

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 provides a neurology disease analysis method and system based on deep learning and a medium. The method comprises the following steps: obtaining clinical examination data and image examination image information, processing to obtain a lesion magnitude evaluation index, obtaining self-described symptom information, extracting illness state high-correlation parameter data in combination with the clinical examination data, processing to obtain an illness state evaluation index, and processing to obtain an illness state development evaluation index in combination with the lesion magnitude evaluation index. Operation data of each detection device and medical ability evaluation data of doctors are obtained and processed to obtain detection accuracy influence factors, family medical history data, gene detection data and personal data are obtained and processed to obtain disease development correlation factors; according to the disease development evaluation index, the detection accuracy influence factor and the disease development correlation factor, processing is carried out to obtain a disease risk evaluation index, and the disease prediction level is determined through threshold comparison, so that the timeliness and accuracy of disease prediction are improved based on deep learning according to the examination data and multiple influence factors.

The invention discloses a knee joint fraction prediction method and system fused with multi-modal image features. The knee joint fraction prediction method fused with the multi-modal image features obtains a knee joint prediction fraction of an object through five steps. The method has the beneficial effects that 1, deep learning, radiomics characteristics and combined clinical characteristics are fused, the magnetic resonance T1 sequence image of the knee joint is processed and analyzed, the popliteal artery ROI is marked, and related radiomics and deep network characteristics are extracted; compared with traditional single feature analysis, the method has the advantages that more comprehensive and accurate disease assessment is provided, and the accuracy and reliability of knee osteoarthritis prediction are improved. And 2, the method is based on the obvious relation between the popliteal artery and the knee bone joint, innovatively analyzes the image features of the popliteal artery and combines clinical data, so that the method not only can predict the occurrence and progress of the osteoarthritis more accurately in an earlier stage, but also obviously improves the prediction effect.

The invention belongs to the technical field of bridge disease diagnosis, and particularly discloses and provides a bridge disease diagnosis and evaluation method based on a monitoring and sensing technology, which comprises the following steps: monitoring structure diseases, material diseases and traffic diseases of a bridge; respectively confirming a structure disease influence index, a material disease influence index and a traffic load disease influence index of the bridge, further confirming a disease influence grade of the bridge, and performing corresponding feedback; according to the method, the structural disease influence index of the bridge is determined according to the structural information of each bridge part, so that the attention on bridge deformation is improved, the assessment accuracy of the bridge structural disease is guaranteed, the reliability of bridge durability assessment is improved, the prediction timeliness of the development trend of the bridge disease is guaranteed, and the prediction efficiency is improved. And meanwhile, comprehensive bridge disease diagnosis is carried out in multiple dimensions according to the structure disease, the material disease and the traffic load disease of the bridge, so that the effectiveness of bridge disease assessment is guaranteed.

The present invention relates to a multi-modal colorectal cancer pre-diagnosis information processing method, system, medium, and device. By converting image information into first-modal features, first clinical information into second-modal features, and family history information into third-modal features, and then performing feature fusion and inputting it into a neural network model of deep learning. Finally, the first pre-diagnosis report output can provide information on the disease risk level, stage feature prediction, canceration probability, and treatment plan of the current user. The multi-modal technology is applied to integrate various information sources such as the patient's personal information, image information, first clinical symptoms, and family history information, improving the comprehensive analysis of the colorectal diseases of the current user, enabling doctors to obtain a more comprehensive disease assessment, and thus achieving a more accurate risk level classification, disease stage prediction, and personalized treatment plan formulation, improving the efficiency and accuracy of the diagnosis and treatment process.

The invention relates to a multi-modal colorectal cancer pre-diagnosis information processing method and system, a medium and equipment, and the method comprises the steps: converting image information into a first modal feature, converting first clinical information into a second modal feature, converting family medical history information into a third modal feature, carrying out the feature fusion, and inputting the features into a deep learning neural network model. The finally output first pre-diagnosis report can provide disease risk level information, disease stage feature prediction information, disease cancerization probability information and disease treatment plan information of the current user, and a plurality of information sources such as personal information, image information, first clinical symptoms and family medical history information of the patient are integrated by applying a multi-modal technology; according to the method, comprehensive analysis of the colorectal disease of the current user is improved, so that a doctor can obtain more comprehensive disease assessment, more accurate risk grade division, disease stage prediction and personalized treatment scheme making are further realized, and the efficiency and the precision degree of the diagnosis and treatment process are improved.

The invention provides a disease identification method and device for a dirty ballast bed in a spring melting period, and relates to the technical field of ballast bed disease identification, and the method specifically comprises the steps: building a discontinuous ligand cementation contact model based on a discrete element simulation method, and introducing a temperature-rigidity coupling factor and a temperature-strength coupling factor into the model; the method comprises the following steps: acquiring mechanical response data of a dirty ballast under the action of different temperature gradients, dirty contents and dynamic loads through a partition cascade temperature control large triaxial experimental device, and constructing a critical dynamic strain calculation model by using the mechanical response data based on a plastic stability theory; a ballast bed structure simulation model is established by combining the cemented contact model and the critical dynamic strain calculation model, a ballast bed microscopic contact behavior under the dynamic-melting coupling effect in the spring melting period is simulated, and simulation data are obtained; according to the method, correlation analysis is carried out on simulation data to generate a disease assessment index of the ballast bed, so that the disease occurrence risk is assessed, and the risk dynamic assessment of the dirty ballast bed in the spring melting period is realized.

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.

The invention discloses a rural highway disease monitoring system based on an SAR satellite remote sensing technology. The rural highway disease monitoring system comprises an SAR satellite data acquisition unit, a data receiving unit, a data processing unit, a disease identification unit, a disease evaluation and monitoring unit, an information management unit and a display unit. The SAR satellite data acquisition unit is used for acquiring an SAR image; the data receiving unit is used for receiving SAR image data; the data processing unit is used for processing the received image data; the disease identification unit is used for identifying and monitoring road disease characteristics; the disease assessment and monitoring unit is used for assessing road health conditions and monitoring disease changes; and the information management unit is used for data storage. The system is provided with the information management unit which can store historical data of road disease feature data, so that the disease assessment and monitoring unit can judge a monitoring result in combination with the historical data and real-time data during disease assessment, and road disease analysis is more accurate.

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 a forest ecological disaster collaborative prediction and evaluation method based on multi-task learning, and the method comprises the following steps: S1, collecting multi-modal data of forest ecological disasters, and unifying data features through a time alignment technology and a space alignment technology; s2, a multi-task learning framework is constructed, a dynamic sharing layer extracts sharing features, and a task special module generates input features of fire prediction, pest and disease assessment and climate change modeling; s3, optimizing the shared feature weight by adopting a parrot optimization algorithm; s4, constructing a task dependency graph, distributing inter-task feature weights through a dynamic attention mechanism, and generating a prediction result of each task; s5, a prediction result is verified and stored in a disaster memory bank, and framework parameters are optimized in combination with an incremental learning mechanism; s6, screening features by using a multi-head self-attention mechanism, and optimizing the prediction result of each task; and S7, outputting a prediction and evaluation report. According to the method, multi-modal data processing, multi-task learning and dynamic optimization technologies are combined to realize collaborative prediction of forest disasters.

The invention discloses an AI assisted valvular disease assessment system. According to the system, the AI image optimization technology is utilized, the image quality can be improved under different acoustic window conditions, and high-quality image acquisition is guaranteed. A heart standard section is automatically identified through an intelligent section identification function, and an operator is prompted to obtain an optimal measurement view angle. After a proper tangent plane is obtained, the parameter automatic measurement function can automatically measure the shape, the structure and hemodynamic parameters (including the valve peak flow rate, the trans-valve pressure difference, the reflux area and the like) of the valve. Besides, according to the automatic grading evaluation function, an evaluation conclusion is output by automatically applying a clinical grading standard according to a measurement result, and when the result is inconsistent, an operator can be prompted to review, so that the efficiency and the accuracy of valvular disease evaluation are effectively improved.

The invention discloses a steel box girder bridge apparent disease assessment method and system based on unmanned aerial vehicle inspection. The method comprises the following steps that an unmanned aerial vehicle airport and an unmanned aerial vehicle are deployed on a steel box girder bridge site; the method comprises the following steps: planning a photographing route in an unmanned aerial vehicle flight control system, acquiring a steel box girder picture by adopting an unmanned aerial vehicle matched with an aerial survey camera, and performing three-dimensional reconstruction on the acquired picture to form a three-dimensional'coarse model 'of the steel box girder; calculating the relative flight height, the camera view width and height, and scale factors in the width and height direction; planning a patrol route; the patrol route is imported into an unmanned aerial vehicle flight control system, and an unmanned aerial vehicle is matched with an aerial survey camera to collect apparent coating pictures of steel box girder webs and bottom plates; coating disease photos of various steel box girders are collected, a target detection network model is constructed, and coating disease types and areas are identified based on a target detection algorithm; according to the invention, automatic identification of the apparent coating diseases of the steel box girder is realized, the deterioration degree of the coating can be automatically evaluated, a detection report can be automatically generated, the detection quality is ensured, and the detection efficiency is improved.

The present disclosure provides systems and methods for disease assessments. In some examples, the present disclosure provides a method for processing or analyzing a bodily sample of a subject. The method may comprise analyzing said bodily sample to yield a data set comprising at least one genetic polymorphism in the bodily sample. The at least one genetic polymorphism may be associated with a pulmonary or respiratory disease or disorder. The method may further comprise computer processing the data set and a clinical health parameter of the subject to determine a risk-value of the pulmonary or respiratory disease or disorder in the subject. The method may further comprise electronically outputting a report that identifies the risk-value of the pulmonary or respiratory disease or disorder in the subject.

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.

A method to treat a predetermined subject population diagnosed with Alzheimer's disease (AD), including: selecting subjects diagnosed with early AD or having at least one symptom of early AD, with a goal to improve a score of the subjects in an assessment scale in at least one point, wherein the assessment scale comprises at least one of, an Alzheimer's Disease Assessment Scale– Cognitive Subscale (ADAS-Cog) or variations thereof, and / or a Mini-Mental State Examination (MMSE) scale or variations thereof; delivering auricular VNS treatment to the subjects for a time period of at least one month; identifying an improvement of at least one point in the scale in the subjects following the delivering.

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.