93 results about "Vascular risk" patented technology

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

The invention provides a multi-parameter collaborative cardiovascular health monitoring system and method, and the system comprises a millimeter wave radar collection module which is used for collecting a multi-dimensional physiological signal of a target user; the third-stage signal processing module is used for carrying out noise reduction processing on the multi-dimensional physiological signals and carrying out layered feature extraction on the multi-dimensional physiological signals subjected to noise reduction to obtain multi-dimensional physiological feature parameters; and the fourth-order crowd adaptation AI evaluation module is used for carrying out crowd segmentation on the target user to obtain segmented crowds, dynamically adjusting the weight ratio of each feature parameter in the multi-dimensional physiological feature parameters according to the segmented crowds to obtain dynamic adjustment feature parameters, and carrying out blood pressure calculation, angiosclerosis calculation and vascular risk prediction according to the dynamic adjustment feature parameters. And obtaining a blood vessel health monitoring result. According to the embodiment of the invention, through software optimization and measurement technology expansion, the cardiovascular health monitoring accuracy and the application range are further improved.

The invention discloses a heart tension index dynamic calculation and risk assessment method based on HRV, and the method comprises the steps: carrying out the filtering and denoising of an electrocardiosignal through a Butterworth filter, and obtaining a filtered electrocardiosignal; calculating interval data of adjacent wave crests; processing the interval data according to fast Fourier transform to obtain frequency domain features, and combining the interval data to obtain time domain features; performing normalization processing on the time domain and frequency domain features based on a maximum and minimum normalization method; calculating a cardiac tension index according to the normalized features and a pre-trained multiple linear regression model; matching a pre-constructed reference database according to the individual age, gender and body mass index to obtain a reference cardiac tension index; calculating a cardiovascular risk score in combination with the normalized frequency domain features and a reference cardiac tension index; and repeating the above steps regularly, smoothing through a moving average method based on continuous multiple cardiovascular risk scores, and analyzing the trend to send out cardiovascular risk early warning information.

The invention relates to the field of biosensors, and discloses a cardiovascular data monitoring method for the cardiovascular medicine department. According to the method, electrocardio, respiration and blood oxygen signals are synchronously collected through a non-invasive terminal, sleep apnea events are analyzed and recognized in a combined mode, the electrocardio signals are deeply analyzed to detect cardiovascular abnormalities, and time sequence characterization of the function state of the autonomic nervous system is generated based on heart rate variability; and further constructing a time sequence causal reasoning model, quantifying the triggering effect of the respiratory event on the cardiovascular event, generating a comprehensive night cardiovascular risk layering index, and triggering graded early warning. According to the invention, non-sensitive, long-time-history and high-precision night cardiovascular risk dynamic assessment and causal mechanism analysis are realized.

The invention discloses a traditional Chinese medicine cardiovascular health assessment method based on tongue picture image recognition, and the method comprises the following steps: collecting original tongue picture image data, and carrying out the image preprocessing; collecting lingual surface spectrum original data, and executing spectrum preprocessing; extracting features of the standardized tongue picture image data, and generating tongue picture image feature vectors; extracting features of the standardized lingual surface spectral data, and generating lingual surface spectral feature vectors; constructing multi-modal hidden space representation of the tongue picture image feature vector and the tongue surface spectrum feature vector, and executing feature decoupling and feature fusion processing; establishing a mapping relation between the tongue picture-spectrum fusion implicit vector and a cardiovascular health index, and generating a cardiovascular risk prediction vector; and generating a cardiovascular health risk assessment result. According to the invention, tongue image and spectral analysis are fused, cardiovascular health assessment is realized, and the method has the advantages of high accuracy and high stability.

The invention discloses a cardiovascular risk early warning method based on multi-modal time sequence data, relates to the technical field of medical health information monitoring, and aims to solve the problem of confusion of causes of dyspnea at night by constructing a cross-modal direction and time delay relation in a sliding time window. Phenotype similar phenomena such as pure blood oxygen reduction / wake-up are decomposed into a comparable time sequence interaction structure, so that a blocking chain type process and a non-blocking type heart failure related process can be distinguished on the structural level, and therefore false alarm and missing alarm caused by confusion are reduced. Meanwhile, a multi-channel signal is firstly converted into a time sequence causal diagram, and then in-window statistical characteristics are combined for judgment, so that the model not only utilizes the self change of each channel, but also utilizes the interaction evidence of the first and second channels, the influence direction and the delay length, and the expression ability of the pathophysiological chain difference is improved.

The invention discloses an acute myocardial infarction cardiovascular risk prediction method based on artificial intelligence. The method comprises the following steps: collecting clinical data of a patient and integrating the clinical data into an initial data set; performing cleaning, missing value processing and classification processing on the data initial set, and constructing a standardized feature data set; a competition risk outcome type is defined according to clinical follow-up visit data of the patient, and competition risk label data is constructed; an improved DeepHit model is established; training and cross validation are carried out by using the standardized feature data set and the competitive risk label data, and a training completion model is determined; and predicting the individual competition risk probability of the patient, and generating a risk level and an early warning prompt. According to the invention, individualized dynamic risk prediction is realized, and clinical diagnosis and treatment decision reliability is enhanced.

The invention relates to a CVD risk assessment tool based on wearable device data and a machine learning algorithm, and the tool comprises a data collection module which obtains cardiovascular traditional risk factors through a cardiovascular risk assessment scale, and obtains monitoring data through a wearable device; the feature screening module is used for determining a candidate predictive factor range, and then performing feature extraction and predictive factor screening on the crowd wearing the wearable equipment by jointly using a minimum absolute contraction and selection operator (LASSO), a random forest (RF) and a Logistic regression model; the model building module is used for building a model based on an XGBoost machine learning algorithm; and the risk diagnosis module is used for outputting and evaluating the probability that the CVD risk of the individual is high within 10 years based on the characteristic data obtained by the wearable equipment data in real time. The cardiovascular health condition of an individual can be dynamically evaluated, personalized and timely risk early warning is provided, the method is suitable for daily health management, remote monitoring and other scenes, and a new solution is provided for early intervention and personalized medical treatment of cardiovascular diseases.

Pulse wave velocity (PWV) is a measure of arterial stiffness and a cardiovascular disease risk factor. Accurate PWV estimation is difficult due to complex arterial dynamics, such as longitudinal motion and natural tissue oscillations. This present disclosure relates to a robust, motion-resistant PWV estimation framework including: 1) tracking and compensating for arterial longitudinal wall motions, 2) suppressing undulating arterial motion via common mode rejection, and 3) enhancing differential signals to extract wall expansion. In vitro experiments with induced lateral motion indicate the framework's PWV estimates (6.19±0.33 m / s) closely matched reference values (6.26±0.12 m / s; error: 1.1%), outperforming methods without motion compensation (4.46±1.78 m / s; error: 28.8%). In vivo trials with five healthy subjects showed an average PWV of 4.18±0.56 m / s using the motion-resistant method, compared to 2.54±0.95 m / s without motion compensation (p<0.005). This framework enhances PWV estimation reliability, offering clinical potential for better arterial stiffness assessment and cardiovascular risk stratification.

The invention relates to the field of cardiovascular risk identification, and particularly discloses an intelligent cardiovascular risk assessment system and method. According to the method, multi-dimensional time sequence data in a human physiological system is converted into a dynamic graph capable of explicitly representing the collaborative relation among physiological indexes in each time window, and then a dynamic graph sequence reflecting continuous evolution of a system state is constructed. On the basis, depth feature extraction and fusion of the space-time dimension are carried out on the dynamic graph sequence so as to comprehensively evaluate the evolution trend of the system stability. By capturing the dynamic evolution trajectory of the physiological network topology structure, the scheme can quantify the subtle process of converting the system from a healthy steady state to an unstable state, thereby identifying an early instability signal which is reflected by multi-dimensional relevance drift and is difficult to perceive on a single index, and further realizing system instability risk scoring and asymptomatic risk early warning.

The application discloses a cardiovascular maintenance functional wine based on a composite fermentation process and a preparation method thereof. Through synergistic fermentation of rosewood flavones, five-grain nutrients and rhizoma notopterygii blood-activating components, a cardiovascular maintenance wine with multiple effects of blood pressure regulation, microcirculation improvement, nerve protection and anti-inflammation is developed. Process parameters are optimized and designed to ensure efficient reservation and release of active components, and the cardiovascular maintenance wine is suitable for daily auxiliary conditioning of sub-healthy people and cardiovascular risk groups.

The invention relates to the technical field of medical care, in particular to a cardiovascular disease risk dynamic early warning system based on multi-source data fusion, and the system comprises the steps: obtaining ejection fraction, coronary artery stenosis percentage and calcification integral data of cardiovascular examination of a monitored object, and heart rate data and pulse pressure difference data of periodic signs; determining a physical examination abnormal coefficient according to the difference between the ejection fraction and the standard value and the difference between the calcification integral data and the standard value and the coronary artery stenosis percentage; according to the height and slope difference of the ST wave band and the PR wave band, the heart rate analysis abnormal index is determined by combining the time interval of the adjacent electric activity wave bands; according to the time sequence change correlation of the heart rate data and the pulse pressure difference data, a physical sign abnormal coefficient is determined; determining a dynamic risk score in combination with the physical examination abnormal coefficient and the physical sign abnormal coefficient; and carrying out risk early warning based on the dynamic risk score. According to the invention, fusion analysis of multi-source and multi-dimensional data can be realized, and the dynamic early warning precision of cardiovascular risks is improved.

Machine Learning (ML)-based systems and methods are described for predicting cardiovascular disease of users of specific geographic regions. In various aspects, user- specific cardiovascular data of a user may be input into an ML model trained with data of a plurality of cardiovascular risk factors specific to a population of given geographic region. The plurality of cardiovascular risk factors is subdivided into a first training data subset (preselected factors) and a second training data subset (remaining factors). The user-specific cardiovascular data of the user as input into the ML model is data of the user corresponding to the preselected subset of cardiovascular risk factors and the remaining subset of cardiovascular risk factors. The ML model outputs a user-specific cardiovascular prediction of the user. The user-specific cardiovascular prediction comprises a cardiovascular risk score of the user. The cardiovascular prediction is displayed on a graphical user interface (GUI).

Aspects of disclosure relate to a composition and methodology for treating menopause symptoms utilizing a cannabinoid-based nanoplatform composition. The composition includes phytocannabinoids like CBD, CBG, and CBN, alongside isoflavones and polyphenols, all integrated into nanoplatform designed for enhanced bioavailability and controlled release. The formulation is designed to alleviate key menopause-related issues, including vasomotor symptoms (VMS), genitourinary syndrome of menopause (GSM), bone loss, mood disturbances, and sleep disorders, while addressing cardiovascular disease (CVD) risks. The composition combines cannabinoids, such as CBD, with isoflavones and polyphenols, and is incorporated into nanoplatforms for enhanced delivery and efficacy. Additionally, the disclosure includes a kit comprising oral capsules, intravaginal ovules, and instructions for use. The capsules contain a blend of cannabinoids, flavonoids, and polyphenols, while the ovules are composed of CBD and cocoa butter. The kit provides a comprehensive solution for managing menopause symptoms and mitigating cardiovascular risks.

The application discloses a cardiovascular chronic disease data management method based on artificial intelligence, relates to the technical field of artificial intelligence medical management, and comprises the following steps: collecting patient multi-element heterogeneous medical data, normalizing and converting structured and semi-structured data, extracting key features of unstructured medical image data and vectorizing and encoding the key features, and generating a standardized cardiovascular chronic disease comprehensive data cube; calling a pre-trained multi-modal cardiovascular risk assessment model to analyze the data cube, and generating a comprehensive risk assessment report containing a risk level, a contribution factor, an evolution trend and an individualized early warning threshold; combining a clinical path knowledge base to generate an individualized health management plan, continuously collecting plan execution feedback data, dynamically updating the data cube, and iteratively optimizing the assessment report and the management plan. The method realizes multi-element heterogeneous data regularization, enriches risk assessment dimensions, realizes dynamic adaptation of a health management plan, and improves the standardization and individualization level of cardiovascular chronic disease data management.

The application discloses a magnetocardiogram gradient instrument and an electrocardio collection circuit packaging structure and a signal processing system, relates to the technical field of medical equipment, and realizes high-density integration of hardware and reliable protection through a rigid protection frame formed by a circuit chip, a layer plate structure and a connecting assembly, shortens a signal transmission path, reduces the influence of environmental vibration, dust, moisture and electromagnetic disturbance on a synchronous sampling link, and provides a stable data basis for multi-modal fusion imaging. The signal processing system comprises multi-modal synchronous collection, noise suppression, data fusion imaging and a diagnosis auxiliary module, generates a high-precision space-time dynamic imaging atlas by constructing a cardiac electromagnetic field joint forward model and applying time and space double constraints, and outputs a cardiovascular risk assessment, a cardiac arrhythmia positioning and a myocardial ischemia quantification result in combination with machine learning. The application solves the problems of low integration, poor anti-interference and insufficient single modal detection precision of existing equipment.

The application discloses a portable detection kit for assisting in predicting a cerebral vascular risk, a blood sample is processed through a sample processing module and input corresponding test paper, a detection result of the test paper is collected by a biological sensing module, and the detection result is calibrated and temporarily stored through an electronic control module, so that a user can acquire the calibrated detection result through a target device in an NFC touch mode and automatically send the calibrated detection result to a cerebral vascular disease monitoring digital platform, cerebral vascular risk prediction is performed by the cerebral vascular disease monitoring digital platform, and the portable detection kit has the beneficial effects of high portability, simple operation and high detection result accuracy.

The present invention provides a method and system for constructing a large multimodal medical model. The method comprises: acquiring multiple sample images; performing noise reduction on the sample images and generating images of each modality based on the noise reduction results; defining a reference coordinate system, mapping the myocardial contour and thickness in a second MRI image to the reference coordinate system, and registering the metabolically active region in a second PET image to the reference coordinate system to obtain a fused coordinate system; generating multimodal features based on vascular topology information, myocardial contour and thickness, and metabolically active regions in the fused coordinate system; annotating the multimodal features corresponding to each sample image as a cardiovascular disease risk level, and inputting the annotated multimodal features into a cardiovascular disease prediction model for training. The present invention can improve the accuracy and reliability of cardiovascular risk prediction.

The invention relates to a hypertension closed-loop management system and method based on a blood pressure load and a multi-dimensional portrait. The method comprises the following steps: acquiring blood pressure time sequence data of a user; processing the blood pressure time sequence data according to a preset clinical threshold value, and calculating a blood pressure load index BP Burden; according to the blood pressure time sequence data, the blood pressure load index BP Burden and other clinically common blood pressure indexes, a multi-dimensional quantitative evaluation label is generated according to a preset evaluation rule; comprehensively judging the blood pressure management state of the user according to the quantitative evaluation label, and matching a preset intervention strategy knowledge base to generate personalized intervention suggestions; and presenting the quantitative evaluation label, the blood pressure management state and the intervention suggestion to a user or a doctor end, and receiving execution feedback to form closed-loop management, and the method has the advantages that the technical effects of accurate prediction of cardiovascular risks and dynamic optimization of blood pressure control quality are achieved.

The invention discloses an image segmentation method for accurate recognition of liver tumor boundaries. The method comprises the following steps: acquiring pathological images at different time periods; segmenting through a deep learning network to obtain a target area containing main blood vessels and tumor boundaries; performing differential fitting on the pathological image, and predicting a safety boundary; judging whether the distance between the safety boundary and the tumor boundary is smaller than a first safety distance or not, and if yes, calculating to obtain an undercut margin rate; if the under-cut edge rate is greater than a first threshold value, performing weighted expansion on the security boundary, calculating the under-cut edge rate again, if the under-cut edge rate is greater than the first threshold value, performing weight reduction iteration, and stopping iteration until the first threshold value is met or the number of iterations is reached; calculating a blood vessel distance between the safety boundary after iteration and the main blood vessel, and if the blood vessel distance is smaller than a second safety distance, marking as a blood vessel risk point; and outputting a target area containing the tumor boundary, the vascular risk point and the safety boundary. According to the invention, the residual risk can be reduced, and the dual requirements of complete resection and function retention are considered.

The invention provides a multi-modal imaging system fusing physiological parameter monitoring, and the system comprises an ultrasonic collection module which collects ultrasonic detection signals according to a preset time sequence; the physiological acquisition module is used for acquiring time sequence reflecting physiological signals according to a preset time sequence; the feature fusion module is used for carrying out time registration on the ultrasonic detection signals and the time sequence physiological signals, extracting multi-modal features and fusing the multi-modal features to form a joint feature vector; the multi-modal estimation module is used for analyzing and obtaining an imaging result and multi-modal physiological parameters of the target tissue or blood vessel based on the joint feature vector; and the output module is used for outputting the imaging result and the multi-modal physiological parameters. According to the method, ultrasonic detection signals and various time sequence physiological signals are deeply fused, so that global accurate capture of vascular structures, dynamic characteristics and physiological time sequence information is realized, and a full-link closed loop of structural imaging, functional imaging and multi-parameter continuous monitoring is successfully cut through; and a high-precision, multi-dimensional and explainable solution is provided for cardiovascular risk assessment and individualized health management.

The invention discloses an electrode implantation path generation method and a related device, and relates to the field of path planning, and the method comprises the steps: obtaining a multi-modal image, carrying out the registration operation of the multi-modal image, and obtaining a registered image; performing region segmentation on the registered image to obtain a brain anatomical structure, a brain blood vessel and a brain function region, and fusing the brain anatomical structure, the brain blood vessel and the brain function region in the same three-dimensional brain model to obtain a fused three-dimensional brain model; obtaining a target region, and performing region boundary adjustment on the target region based on the fused three-dimensional brain model, so that the boundary of the target region is within the range of the function association region, and / or the distance between the boundary and the adjacent region is not less than a safety threshold; and on the basis of the vascular risk penalty term, generating an electrode implantation path from a needle insertion point to the target area by taking the path length from the needle insertion point of electrode implantation to the target area is smaller than a preset length as a target. The electrode positioning precision can be improved.

A method of lowering LDL-C levels, reducing cardiovascular risk and / or treatment of a cardiovascular disease, comprising administering to a subject in need thereof, a first amount of AZD0780 or a pharmaceutically acceptable salt thereof and a second amount of ezetimibe or a pharmaceutically acceptable salt thereof, wherein the first amount and the second amount together comprise a therapeutically effective amount.

The present disclosure provides methods for determining whether a subject has platelet hyperreactivity. Further provided are methods for determining risk of developing a platelet-mediated cardiovascular and / or limb event in a subject, as well as determining whether a subject has a condition associated with platelet hyperreactivity. Still further provided are methods for preventing a cardiovascular and / or limb event in a subject in need thereof; treating or preventing a condition associated with platelet hyperreactivity in a subject in need thereof; and, monitoring the progression of a condition associated with platelet hyperreactivity in a subject diagnosed with the condition. Methods for determining the effect of an antiplatelet treatment on development of a condition associated with platelet hyperreactivity in a subject diagnosed with the condition, and identifying an antiplatelet treatment useful for slowing down the progression or treating a condition associated with platelet hyperreactivity in a subject diagnosed with the condition are also provided.

Apparatus and methods are provided for determining a cardiovascular risk score from at least cardiovascular data. [Solution] The method includes providing a device adapted to measure a cardiovascular signal of a user, measuring the cardiovascular signal using the device during an observation period having a duration of at least 24 hours, each observation period having a duration of 24 hours having a plurality of observation periods, determining a cardiovascular value for each observation period, compiling the determined cardiovascular values ​​for corresponding measurement periods of each observation period into a set of cardiovascular parameters, creating a 24-hour circadian plot of the set of parameters against the corresponding measurement periods, determining a physiological parameter of the user using the circadian plot, and calculating a cardiovascular risk score for the user using the determined physiological parameter.

A device for predicting and diagnosing risk by cardiovascular disease type according to an embodiment comprises at least one processor, wherein the at least one processor predicts risk by cardiovascular disease type using a diagnostic model. The diagnostic model includes a first model that analyzes an inputted fundus image to predict cardiovascular risk; and a second model that receives a cardiovascular risk score predicted by the first model and disease type-specific factors to predict risk by cardiovascular disease type. Wherein the at least one processor is characterized by providing diagnostic information based on the cardiovascular risk predicted by the first model and the risk by cardiovascular disease type predicted by the second model.

This invention discloses a dynamic prediction system for long-term adverse cardiovascular event risk after stent implantation, belonging to the field of personal health risk assessment technology. It includes: a cardiovascular assessment module for determining the target cardiovascular adverse event presentation coefficient based on the target patient's first chest pain rating and real-time heart rate after stent implantation; a cardiac function assessment module for determining the emphasis of cardiac function analysis at each stage based on the target patient's physical examination data after stent implantation; a probability determination module for determining the probability of the first adverse event based on the target cardiovascular adverse event presentation coefficient and the emphasis of each cardiac function analysis; and an indicator determination module for comparing the target patient's first adverse event probability with the second adverse event probability of each monitored patient to obtain the target cardiovascular risk warning indicator for the target patient. This invention can improve the accuracy of long-term risk prediction for adverse cardiovascular events.

Provided herein are methods of treating, preventing, and managing metabolic or cardiovascular disorders and methods of reducing cardiovascular risk with FGF21 protein variants, including Fc-FGF21 variant fusion proteins.

The invention provides a cardiovascular emergency multi-parameter intelligent monitoring method and device, relates to the technical field of cardiovascular risk monitoring, and aims to solve the technical problem of poor risk assessment effect in related technologies. The method comprises the following steps: acquiring various physiological signals; carrying out cardiac cycle segmentation and time alignment on the multiple types of physiological signals according to electrocardiograph R wave peak value time to form multi-parameter time coupling data; extracting a first group of biophysical parameters based on the multi-parameter time coupling data; calculating a second group of signal quality parameters based on the original waveforms of the multiple types of physiological signals; constructing a multi-dimensional physiological risk space based on the first group of biophysical parameters; dynamically calculating a spatial risk aggregation degree index based on the real-time or near-real-time data points of the first group of biophysical parameters in the multi-dimensional physiological risk space; determining a cardiovascular emergency risk level identifier based on the spatial risk aggregation degree index and the second group of signal quality parameters; and outputting a cardiovascular emergency risk level identifier.