882 results about "Coronary arteries" patented technology

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient's heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

The invention relates to a coronary artery plaque rupture risk assessment method based on a non-local physical information neural network, and belongs to the field of artificial intelligence, and the method comprises the following steps: S1, introducing a near-field dynamics theory, reconstructing a mechanical constitutive equation of plaque rupture, and deducing a crack propagation criterion; s2, constructing a non-local physical information neural network, taking geometric structure information and material attributes of the coronary artery plaque as input, and taking a displacement field and a stress field of the plaque as output; taking the plaque mechanics basic equation as a physical constraint condition, and adding a loss function; s3, constructing a data set; s4, training the physical information neural network by using the training set, and adjusting network parameters by using a stochastic gradient descent optimization algorithm to minimize a loss function; optimizing and testing the model; and S5, inputting a coronary artery medical image number to be evaluated into the trained non-local physical information neural network, and outputting a rupture risk evaluation result of the coronary artery plaque.

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, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, 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 a coronary artery fractional flow reserve non-invasive evaluation method fused with hemodynamics, and belongs to the technical field of medical detection, and the method comprises the following steps: obtaining coronary artery CT image data, carrying out preprocessing, and extracting a coronary artery three-dimensional geometric model; constructing a neural network fused with hemodynamics, and adding a residual term of a Navier-Stokes equation and a residual term of a Murray law into a loss function; collecting a real training sample, and generating numerical simulation data as a training data set; s4, training and optimizing the neural network fused with hemodynamics to minimize an error between an output result of the network and an FFR training value, and satisfying constraint conditions of a Navier-Stokes equation and a Murray law at the same time; and S5, processing the coronary artery CT image data by using the trained network model fused with hemodynamics to obtain a predicted FFR value.

The invention relates to the field of cardiovascular medicine, and discloses a coronary artery vulnerable plaque detection and analysis system which comprises a data collection module used for collecting multi-mode imaging data of a coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery, and a plaque area identification module used for positioning the branch opening position of the coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery. Multi-class plaques of coronary arteries are separated out; the plaque classification detection module is used for extracting structural features of multiple categories of plaques, identifying vulnerable plaques and stable plaques in the coronary artery and setting a plaque classification detection system of the coronary artery; the vulnerable plaque evaluation module is used for constructing a coronary artery vulnerable plaque scoring mechanism according to the plaque vulnerability index, the plaque component and the radial wall strain value; and the result feedback module is used for generating a vulnerable plaque feedback result of the coronary artery in combination with the vulnerable plaque scoring mechanism and the plaque classification detection system. According to the invention, the accuracy of coronary artery vulnerable plaque detection can be improved.

The invention relates to a coronary artery risk prediction method and system for a coronary heart disease patient, and aims to solve the limitation that a traditional static evaluation model is difficult to integrate multi-source dynamic data. Collecting multi-source heterogeneous data of a patient from a hospital information system, a gene database and wearable equipment; the method comprises the following steps: respectively extracting key features through a special processing module: analyzing unstructured text and image plaque features by adopting a deep learning model, calculating a multi-gene risk score, capturing a physiological parameter attenuation trend by utilizing time sequence analysis, constructing a three-stage cascade integration model, screening high-contribution features at the first stage, and constructing a three-stage cascade integration model; the secondary stage integrates space-time dynamic characteristics through an attention mechanism network, the final stage integrates static and dynamic factors to generate a dynamic risk score, a visual risk trend chart and a high-risk factor thermodynamic diagram are output, and real-time early warning and linkage are performed through a medical terminal to generate a personalized intervention scheme. According to the method, collaborative analysis and dynamic risk tracking of multi-dimensional data are realized, and the early warning capability and the clinical decision-making efficiency are remarkably improved.

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

A method is provided for optimizing workflow for a vascular intervention based on a three-dimensional dataset of a vessel, particularly a coronary artery, acquired from a 3D imaging modality, which involves:determining vessel centerline;performing segmentation in the dataset to identify the vessel lumen;performing plaque segmentation in the dataset to identifying the plaque in the vessel wall;defining a parameter related to the plaque severity in the vessel wall; andcreating a two-dimensional image spanning the length of the segmented vessel to illustrate both the plaque severity parameter and its spatial distribution in relation to the vessel wall circumference using the position of points along the centerline in the segmented vessel as primary axis.Other aspects are described and claimed.

The invention belongs to the technical field of medical image data processing, and particularly relates to a cardiac coronary plaque simulation method and system based on a digital phantom, and the method comprises the step of generating reconstructed simulation CT projection images with different halo artifact degrees. The method solves the problems that the effectiveness of an artifact removal method cannot be verified and evaluated due to the fact that the algorithm training effect is not generalized enough, and has the beneficial technical effects that the effectiveness of the artifact removal method is fully verified and evaluated, and the generalization of a deep learning halo artifact removal algorithm is improved.

The invention relates to a hypertrophic obstructive cardiomyopathy virtual resection operation curative effect evaluation method based on individualized computational fluid mechanics, which can give visual postoperative hemodynamic indexes, predict postoperative curative effects, find an optimal resection scheme and realize preoperative accurate intelligent evaluation. Comprising the following steps of: acquiring coronary artery enhanced computed tomography image data, acquiring a coronary artery enhanced computed tomography image of a patient, and outputting and storing the coronary artery enhanced computed tomography image in a DICOM (Digital Imaging and Communications in Medicine) format; preprocessing image data, and segmenting and reconstructing a left ventricle-outflow tract structure three-dimensional model; setting a virtual excision scheme at the fleshy part; constructing a surgical resection model, and importing the model into finite element analysis software for solving; and the virtual postoperative curative effect is visualized. Verification and curative effect prediction of a patient excision scheme can be realized, and the operation success rate of a hypertrophic obstructive cardiomyopathy patient is improved.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

The disclosure provides methods of reducing the risk of a major adverse cardiovascular event (MACE) in a subject having previously experienced acute coronary syndrome (ACS) and for reducing risk of a MACE in a patient who is diagnosed with coronary artery disease and has blood high-sensitivity C- reactive (hs-CRP) of >2.0 mg / L. The methods include administering to the subject an anti-oxLDL antibody. The anti-oxLDL antibody may be administered to the subject alone or in combination with one or more additional therapeutic agents, such as one or more lipid lowering agents.

The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and / or estimate a metric associated with the presence, non-presence, severity, and / or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.

A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and / or outer walls of the coronary arteries of the patient.

The invention relates to the technical field of medical information, in particular to a coronary artery stenosis intelligent diagnosis system and method based on deep learning, and realizes efficient and accurate diagnosis through data acquisition, marking, convolutional neural network model construction, evaluation and acceleration modules. The system preprocesses the contrast image, marks the narrow position and degree, constructs a convolutional neural network model combined with an attention degree graph, and optimizes parameters to improve the accuracy; through parallel processing, the diagnosis time is shortened, the problems of an existing diagnosis method are systematically and innovatively solved, and the method has high accuracy, high efficiency and good interpretability and is expected to become an important clinical auxiliary tool for early screening and emergency diagnosis of the coronary heart disease.

The invention discloses a liver transplantation perioperative concurrent myocardial ischemia risk assessment method and system, and the method comprises the steps: obtaining coronary artery image data of a liver transplantation patient, carrying out the three-dimensional reconstruction according to the coronary artery image data, and generating a personalized coronary artery geometric model; based on the geometric model of the coronary artery, performing numerical simulation on the flowing condition of blood flow in the coronary artery by utilizing a computational fluid mechanics method, obtaining the speed and pressure distribution of the blood flow of the coronary artery through the numerical simulation, and calculating the pressure ratio before and after the coronary artery stenosis; evaluating the coronary ischemia condition according to the pressure ratio, and generating a fractional flow reserve value and a myocardial ischemia risk evaluation report. The coronary artery blood flow reserve score value is calculated by simulating the coronary artery blood flow conditions under different physiological conditions, so that the myocardial ischemia risk possibly faced by a patient during an operation is comprehensively evaluated, and intra-operation decision support is provided for clinicians.

The invention relates to the technical field of medical data processing, and discloses a non-invasive hemodynamic assessment system for coronary artery stenosis degree, comprising an image acquisition module used for acquiring a coronary artery computed tomography angiography image of a patient; the physiological signal acquisition module is used for synchronously acquiring physiological signals of a patient, and the physiological signals comprise electrocardiosignals and non-invasive arterial blood pressure signals; and the data processing module is used for preprocessing and segmenting the coronary artery computed tomography angiography image. Electrocardiogram and non-invasive arterial blood pressure signals are collected through the sensor, risks and complications caused by invasive examination of traditional coronary artery radiography are avoided, the problem that patients unsuitable for invasive examination such as contrast agent allergy and renal insufficiency are difficult to evaluate is solved, the pain of the patients is relieved, and the acceptability of the patients is improved.

The invention provides a three-dimensional semi-supervised segmentation method and device for a stent in a coronary artery. The method comprises the steps that an initial image to be segmented is input into a three-dimensional semi-supervised segmentation deep learning network model trained in advance, the three-dimensional semi-supervised segmentation deep learning network model comprises a shared encoder, a BiFormer module and a decoding module which are connected in sequence, and the decoding module comprises a plurality of decoders adopting different attention mechanisms; the average value of result prediction probabilities output by all decoders in the decoding module is obtained, a prediction result is obtained, and three-dimensional segmentation of the stent in the coronary artery is achieved; in the model training process, model parameter optimization is carried out through a plurality of generated prediction output results with perception deviation and a cross pseudo-supervised training strategy. According to the three-dimensional semi-supervised segmentation deep learning network model designed by the invention, the problem that model training lacks annotation supervision is solved, model parameters are optimized through a cross pseudo-supervised training strategy, and the problem that segmentation of a support depends on the number of annotated data is solved.

The invention relates to the field of medicine synthesis, and discloses a GLP-1 / GIP / GCG tri-agonist compound. The GLP-1 / GIP / GCG three-agonist compound is used for preparing a medicine composition for treating diseases, and the application of the medicine composition in preparing a medicine for treating at least one of the following diseases, namely, the GLP-1 / GIP / GCG three-agonist compound, and the GLP-1 / GIP / GCG three-agonist compound. The diseases include type II diabetes mellitus, impaired glucose tolerance, type I diabetes mellitus, obesity, hypertension, metabolic syndrome, dyslipidemia, cognitive impairment, atherosclerosis, myocardial infarction, coronary heart disease, cardiovascular disease, stroke, inflammatory bowel syndrome and / or dyspepsia or gastric ulcer, hepatic fibrosis disease and pulmonary fibrosis disease.

The invention discloses a coronary artery vulnerable plaque segmentation model training method and device based on multi-mode semi-supervised learning. The method comprises the following steps: acquiring a CTA coronary blood vessel image set and a blood vessel inner cavity image set; respectively preprocessing each group of paired images so as to obtain preprocessed paired images; constructing a registration model and a coronary artery vulnerable plaque segmentation model based on a semi-supervised learning framework; and respectively training the registration model and the coronary artery vulnerable plaque segmentation model based on the semi-supervised learning framework so as to obtain the trained registration model and the trained coronary artery vulnerable plaque segmentation model based on the semi-supervised learning framework. According to the coronary artery vulnerable plaque segmentation model based on multi-modal semi-supervised learning, the defect of a traditional registration method depending on a manual design loss function can be overcome.

The invention relates to the field of medicine synthesis, and discloses a GLP-1 / GIP / GCG tri-agonist compound. The GLP-1 / GIP / GCG three-agonist compound is used for preparing a medicine composition for treating diseases, and the application of the medicine composition in preparing a medicine for treating at least one of the following diseases, namely, the GLP-1 / GIP / GCG three-agonist compound, and the GLP-1 / GIP / GCG three-agonist compound. The diseases include type II diabetes mellitus, impaired glucose tolerance, type I diabetes mellitus, obesity, hypertension, metabolic syndrome, dyslipidemia, cognitive impairment, atherosclerosis, myocardial infarction, coronary heart disease, cardiovascular disease, stroke, inflammatory bowel syndrome and / or dyspepsia or gastric ulcer, hepatic fibrosis disease and pulmonary fibrosis disease.

The invention provides a coronary artery three-dimensional reconstruction method based on a foresight intravascular ultrasound image sequence, and the method comprises the steps: carrying out the lumen segmentation of a foresight intravascular ultrasound image frame sequence, recognizing a blood vessel branch opening, and marking a branch frame; the method comprises the following steps: acquiring coordinates of an initial blood vessel center line and a blood vessel edge point based on a lumen contour, constructing a four-dimensional control point associated with a space coordinate and a radius, and carrying out joint optimization on a three-dimensional blood vessel trajectory and the radius by adopting a spline function of a cost function balance data fitting error and trajectory smoothness; calculating a matching error based on the physical length of the branch blood vessel, circularly matching the branches in the image frame sequence and the branches of the optimized blood vessel center line, realizing automatic key point alignment and determining the spatial position distribution of the frame sequence; sparse lumen contour points are converted into a continuous voxel gray field through distance weighted interpolation, the implicit surfaces inside and outside a lumen are defined by combining a symbol distance function, and a smooth and continuous three-dimensional blood vessel model is obtained through reconstruction by adopting an advancing cube algorithm.

The invention relates to the field of medical image processing, in particular to a medical image segmentation method based on improved U-Net. According to the method, an improved U-Net network structure and an innovative training strategy are introduced, and an sE attention module and multi-scale expansion convolution are introduced into each layer of an encoder, so that semantic information of a multi-scale defect target is effectively captured. An additive attention mechanism is fused in each layer of the encoder to enhance the global spatial perception capability of decoder features while optimizing the expression of detail information. According to the method, an accurate coronary artery segmentation result can be provided for a doctor, and disease diagnosis and formulation of a treatment plan are facilitated.

The invention relates to the technical field of model reconstruction, in particular to a method for reconstructing a coronary artery model based on coronary artery angiography. The method comprises the following steps: acquiring a coronary artery scanning image of a patient; performing three-dimensional coronary artery space structure construction on the coronary artery image of the patient to generate a three-dimensional coronary artery space model; performing blood vessel diameter analysis on the three-dimensional coronary artery space model to generate coronary artery blood vessel diameter data; acquiring an X-ray angiography image of a patient; performing coronary artery vein structure construction on the X-ray angiography image of the patient to generate a three-dimensional coronary artery vein model; and performing blood vessel wall thickness analysis on the three-dimensional coronary artery vein model to generate coronary artery blood vessel wall thickness data. According to the method, multi-modal modeling is carried out by combining the coronary artery scanning image and the X-ray angiography image of the patient, anomaly analysis is carried out on the coronary artery, and the accuracy and reliability of coronary artery model reconstruction are improved.

The invention provides an interpretable machine learning-based giant coronary artery tumor prediction method in Kawasaki disease, and belongs to the technical field of biological information processing. Comprising the following steps: S1, collecting hospitalized KD child patient cases by adopting a retrospective queue research method, and excluding cases which do not meet requirements; s2, coronary artery lesion evaluation and classification; s3, performing variable preprocessing; s4, constructing a machine learning model; s5, performing variable screening; s6, model interpretation; s7, determining an optimal intervention threshold value; and S8, developing an online prediction tool. According to the method, an SVM kernel lab model with the optimal performance is screened out through multi-model comparison, and a group of key prediction molecules are determined in combination with an SHAP method and recursive feature elimination; based on a multi-center data verification model, the model applicability is improved; and early recognition and layered management of high-risk child patients can be realized. A webpage tool is developed, and a patient with high risk is suggested to intervene in advance.

The invention discloses a blood vessel OCT image training method, a blood vessel OCT image generating method and a blood vessel OCT image training device based on a DCN cross-modal fusion network and structure-blood flow coupling modeling. The method comprises the following steps: acquiring image data marked with a blood vessel segmentation result and a plaque position; acquiring the preprocessed OCT image data; registering the image data marked with the blood vessel segmentation result and the plaque position and the OCT image data so as to obtain registered image data; acquiring hemodynamic parameters; inputting the coronary artery CTA image, the image marked with the blood vessel segmentation result and the plaque position and hemodynamic parameters into a cross-modal fusion network based on deformable convolution so as to obtain CTA features of the cross-modal fusion network; and inputting the CTA features of the cross-modal fusion network into a trained OCT generation network so as to obtain a cross-section OCT image containing blood vessel wall and plaque information. The method can replace part of invasive OCT examination, assist doctors in assessing plaque vulnerability and guide interventional therapy.