118 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 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 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 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.

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 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 mental disease assessment method based on visual evoked potential abnormity, which comprises the following steps of: taking chessboard overturning as a stimulation graph, acquiring graphic visual evoked potential by utilizing electrophysiology visual acquisition equipment, and analyzing visual evoked potential waveform by utilizing an assessment module; and finally comparing whether the difference between the latency parameter and the amplitude parameter in the graphic visual evoked potential has significance or not, and providing a statistical index for a doctor and the patient to indicate the illness degree of the patient or whether the illness state is controlled or not. The method changes the traditional influence of clinical experience of doctors, subjective factors of patients and the like on objectivity of diagnosis results, is repeatable and low in cost, is a non-invasive and rapid evaluation method, and is expected to become a biomarker for mental disease patients and a potential tool for psychiatric diagnosis in the future.

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

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 deep learning-based children pneumonia lung image analysis and evaluation system and method, relates to the technical field of children pneumonia analysis and evaluation, and aims to solve the technical problem that the accuracy of artificial diagnosis is difficult to guarantee in the prior art. Transmitting the data to a data calculation module; the data calculation module is connected to the lung shadow detection module and used for receiving the image of the lung shadow detection module and calculating pulmonary alveolar density, definition, quantity and echo intensity data; the image comparison module is used for comparing and analyzing data obtained by the data calculation module and judging the treatment condition of the child pneumonia patient; the evaluation module is used for evaluating whether the pneumonia of the child pneumonia patient is worsened or improved by applying a comprehensive evaluation algorithm according to a result of the image comparison module; and a treatment processing module. The method has the advantages that the lung image of the child is accurately recognized and judged, and the disease assessment accuracy is improved.

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.