26 results about "Coronary stenosis" patented technology

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 belongs to the field of biological medicine industry, and discloses a peripheral blood marker and system for identifying the severity of viral myocarditis or coronary artery stenosis. The inventor finds for the first time that the average value of LRG1 in blood is not obviously changed compared with that of a normal person when mild and moderate myocarditis and microcirculation angina pectoris with normal coronary artery or coronary artery stenosis smaller than 10% exist, and the blood LRG1 is obviously increased when the coronary artery stenosis of the coronary heart disease exceeds 70%; when severe myocarditis occurs, the blood LRG1 is also obviously increased, so that the increase of the blood LRG1 is a blood marker for patients with severe myocarditis or severe coronary artery lesion, and the blank that blood indexes are lacked in the prior art to prompt the patients to have severe coronary artery stenosis is filled. By detecting the LRG1 content of the peripheral blood and combining with other conventional examinations, the severity of myocarditis or coronary stenosis can be simply, accurately and quickly distinguished, and a corresponding basis is provided for subsequent accurate treatment.

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.

The application discloses a blade cutting balloon suitable for coronary artery stenosis and relates to the technical field of cutting balloons. The balloon body is internally provided with a plurality of groups of cutting blades which are slidably connected to the inner wall of the balloon body and extend to the outside of the balloon body, and the balloon body is internally provided with a fixed pipe which is fixedly connected to the inner wall of the balloon body and is internally provided with a movable block.

This invention discloses a method for predicting coronary artery stenosis wall shear stress based on graph neural networks, comprising the following steps: S1, constructing a graph neural network model based on physical constraints; S2, training the constructed graph neural network model; S3, using the trained graph neural network model to predict the coronary artery stenosis wall shear stress and outputting the prediction result; wherein, in step S2, the overall loss function constrained by the physical constraint function is used to train the model during the training process of the graph neural network model. This invention achieves high-speed and high-precision prediction of WSS distributions for complex or even unseen geometries (such as eccentricity, recurrent / multiple lesions, elliptical cross sections and their combinations) by learning from large-scale samples without explicit CFD solutions.

The invention provides a method for predicting coronary artery stenosis degree based on a tongue picture of a patient with coronary heart disease. The method comprises the steps of obtaining a data set; the data set comprises electronic medical record information, blood examination indexes, cardiac ultrasound indexes, electrocardiograms and tongue pictures of the coronary heart disease patient; dividing the preprocessed data set into a training set and a test set according to a preset proportion; building a deep learning network model; inputting the training set into the deep learning network model for training and optimization, and storing the final optimized deep learning network model; and inputting the test set into the finally optimized deep learning network model to obtain a prediction result of the coronary artery stenosis degree of the coronary heart disease patient. According to the method, the coronary artery stenosis degree is judged according to the tongue picture, the electrocardiogram, the cardiac ultrasound index and the blood examination index of the patient with the coronary heart disease, tongue picture features extracted from the tongue picture are converted into quantifiable coronary artery stenosis noninvasive screening targets, and traditional Chinese and western medicine combined diagnosis and treatment intellectualization is promoted.

The invention discloses an intelligent assessment method and system for coronary stenosis based on chest radiography, and relates to the technical field of health risk assessment, and the method comprises the steps: obtaining chest radiography image data of a target patient; based on the time sequence fluctuation of the calcification focus area in the chest radiograph image data, calculating to obtain a calcification activity index, determining a cardiac pulsation key point in the chest radiograph image data, and based on the displacement change of the cardiac pulsation key point between each image frame, calculating to obtain a collateral circulation compensation index of the calcification focus area; based on the collateral circulation compensation index and the calcification activity index, calculating a dynamic risk factor corresponding to the coronary artery stenosis; and inputting the dynamic risk factor into a preset neural network model for iterative training, and performing prediction processing on the chest radiograph image data based on the trained preset neural network model to obtain a coronary artery stenosis risk assessment result. The technical effect of reducing the clinical misjudgment risk of coronary artery stenosis is achieved.

This specification provides a cardiac ultrasound coronary artery stenosis diagnostic system and method based on a visual language transfer model. The system includes: a database construction module for standardizing multi-view cardiac ultrasound video sequences and radiological reports; a cardiac region of interest (ROI) standardization and labeling module for segmenting myocardial segments, obtaining ROI data, and generating a coronary artery-specific perfusion region mask as the ROI for vascular-level stenosis prediction; a visual language transfer pre-training module for semantic alignment of video data and report text using a visual language transfer model; and a downstream task prediction module for utilizing the enhanced attention mechanism of the coronary artery-specific perfusion region mask, based on the global visual features output by the visual language transfer pre-training module, to output patient-level classification results for obstructive coronary artery disease and vascular-level stenosis prediction results for LAD, LCX, and RCA.

The application discloses a noninvasive calculation method of coronary microcirculation resistance and resistance index, and the method comprises the following steps: firstly, based on coronary computer tomography angiography data, blood vessel segmentation is carried out by using a U-Net convolutional neural network, and then a personalized 3D coronary stenosis branch vessel model is reconstructed; secondly, the information of multiple blood vessels (physiological parameters such as age, blood pressure and left ventricular volume in the systolic phase) is integrated, and the length, cross-sectional area, volume and other geometric structure parameters of a specific part of the blood vessel are measured and calculated; thirdly, fluid dynamics simulation is utilized to obtain the invasive CMR and IMR; fourthly, correlation analysis is carried out between the characteristic parameters and the CMR and IMR; fifthly, the characteristic parameters with high correlation are selected to carry out machine learning prediction, so as to realize noninvasive calculation of the CMR and IMR. The noninvasive method for evaluating the CMR and IMR can complete measurement in a short time and has high precision, and is convenient and safe.

Embodiments of the present application provide a training method, an image segmentation method, a computer-aided diagnosis method and system. The method comprises: obtaining a prediction annotation result and a corresponding sample image, the sample image containing a sample object, the prediction annotation result being obtained from a first prediction segmentation result satisfying a target screening condition, the first prediction segmentation result being output by a first image segmentation model trained to segment the sample object, and the target screening condition being configured based on a training target of a second image segmentation model; inputting the sample image into the second image segmentation model to obtain a second prediction segmentation result output by the second image segmentation model to segment the sample object; and training the second image segmentation model using a loss value between the prediction annotation result and the second prediction segmentation result. Embodiments of the present application can improve the accuracy of coronary artery vessel segmentation of NCCT images and lay a foundation for downstream coronary stenosis detection tasks.

A use of N-lactoyl-phenylalanine (Lac-Phe) in a preparation of a medicament for preventing and / or treating ischemic heart disease is provided. It is the first to demonstrate that Lac-Phe can improve the cardiac function in a mouse model of left anterior descending coronary artery stenosis, promote cardiac angiogenesis, improve the viability of hypoxic cardiomyocytes, and inhibit the expression of inflammatory cytokines in the heart, thereby playing an anti-myocardial ischemic injury role. Therefore, the Lac-Phe can be used to prepare a medicament for treating ischemic heart disease. A novel technical means for the treatment of ischemic heart disease is provided.

The application discloses a kind of based on chest radiograph coronary artery stenosis intelligent evaluation method and system, it is related to health risk assessment technical field, the method includes: obtaining the chest radiograph image data of target patient;Based on the time sequence fluctuation of calcification focus area in chest radiograph image data, the calcification activity index is calculated, determine the heart beat key point in chest radiograph image data, based on the displacement change between each image frame of heart beat key point, the collateral circulation compensation index of calcification focus area is calculated;Based on collateral circulation compensation index and calcification activity index, the dynamic risk factor corresponding to coronary stenosis is calculated;Dynamic risk factor is input to the preset neural network model and is iteratively trained, and based on the preset neural network model after training is completed to chest radiograph image data is predicted to process, obtains coronary stenosis risk assessment result.The application reaches the technical effect of reducing the clinical misjudgment risk of coronary artery stenosis.

The present application relates to the technical field of blood vessel image processing, and particularly relates to a coronary artery stenosis functional evaluation method based on angiography imageomics. According to the characteristic quantitative value of the stenosis degree of each patient in the multi-dimensional image performance characteristics, the stenosis grade of each patient is obtained. According to the distribution of the characteristic quantitative value of the patient in different image performance characteristics in different stenosis grades, the key possibility of the current patient in each image performance characteristic is obtained, and the image performance key characteristics are screened out. According to the distribution of the characteristic quantitative value of each image performance key characteristic of different patients in the stenosis grade where the current patient is located, the coronary artery quality contribution degree of each image performance key characteristic in the stenosis grade where the current patient is located is obtained. The coronary artery stenosis evaluation score of the stenosis grade where the current patient is located is obtained. The present application can accurately obtain the coronary artery stenosis evaluation score of the stenosis grade where the current patient is located, and improve the accuracy of the stenosis functional evaluation.

The invention discloses a system for evaluating coronary artery stenosis through a photon CT low-dose low-concentration contrast agent scheme, and belongs to the field of medical images. The invention discloses a system for evaluating coronary artery stenosis by using a photon CT low-dose low-concentration contrast agent scheme. The system comprises a multi-energy spectrum data acquisition unit, a data preprocessing unit, an image generation unit, an image segmentation unit, a plaque analysis unit and a stenosis detection unit. The problems that in existing coronary artery CT examination, the radiation dosage is high, the contrast agent dosage is large, and the diagnosis process highly depends on physician experience, so that efficiency is low, and subjectivity is high are solved, the photon counting CT detector and optimized low-dosage scanning parameters are adopted, the deep learning algorithm and the multi-material decomposition technology are combined, and the CT examination efficiency is improved. High-quality coronary artery imaging and accurate stenosis evaluation under the conditions of low dosage and low concentration of contrast agents are realized, radiation exposure and contrast agent related risks of patients are remarkably reduced, and accuracy and reliability of coronary artery stenosis detection and plaque component analysis are improved.

The application discloses a coronary stenosis detection method based on coronary angiography and space-time collaborative learning, and belongs to the field of intelligent medical treatment. The deep feature extraction network based on the channel mixing structure reparameterization convolution single aggregation module is used, the channel segmentation and multi-branch training are carried out, the feature reuse and information flow are enhanced, the double-flow path is introduced into the feature fusion network to realize adaptive weighted fusion of different scale features, and the representation of spatial features is improved; the space-time collaborative analysis module capable of simultaneously fusing time information and local space information is used, the time sequence connection is constructed, the space-time feature map is fused, the vascular shape deformation and hemodynamic information in the space-time sequence are effectively captured, the effective feature information is enhanced, and therefore the detection accuracy and robustness of coronary stenosis lesions are improved. The time and space information of the coronary angiography sequence image is comprehensively considered, the number of false positive detections is reduced, and the detection capability of the model for coronary stenosis lesions is comprehensively improved.

An automatic method and system for detecting coronary artery stenosis solves the problems of cumulative error relying on segmentation structures and difficulty in extracting inter-frame correlation information from coronary angiography sequences, thereby quickly and effectively obtaining accurate stenosis detection results and providing more precise stenosis locations for subsequent coronary artery lesion analysis. The method includes: (1) semantic feature map and stenosis proposal extraction, automatically extracting semantic feature maps and stenosis proposals from each frame of the input coronary angiography sequence; (2) spatiotemporal feature aggregation, using semantic feature maps and stenosis proposals to generate spatiotemporal RoI features of the coronary angiography sequence, and performing spatiotemporal feature aggregation by learning the contextual correlation of spatiotemporal RoI features to generate enhanced RoI features; (3) stenosis result prediction, inputting the enhanced RoI features into a multi-task output network to predict the stenosis detection result of the coronary angiography sequence.

Approaches for detecting the presence of coronary artery disease (CAD) using an input eye image of a subject are described. The CAD detection is performed by analyzing the image through machine learning models that detect the presence of at least one of coronary artery stenosis and coronary artery calcium (CAC). Once obtained, the input eye image is processed by two distinct models, i.e., one for stenosis detection and another for CAC detection. Each model extracts values corresponding to a plurality of characteristic attributes from the image, which indicate vascular and morphological details of the fundus of the subject's eye. Based on these extracted values, the models determine the presence of stenosis and / or CAC, which are then used to assess the likelihood or presence of CAD.

Provided is a coronary blood flow measurement device capable of acquiring significant information for determination of ischemic heart disease caused by stenosis or the like in a coronary blood vessel on the basis of measurement of a blood flow (coronary blood flow) in a blood vessel (coronary blood vessel) of a heart. This coronary blood flow measurement device (10) includes: a CFR acquisition means (12) for acquiring a calculated value of coronary blood flow reserve (CFR); and a cCFR calculation means (14) for obtaining a blood pressure correction CFR (cCFR) by converting the calculated value of the coronary blood flow reserve (CFR) acquired by the CFR acquisition means into a value under a condition of an arbitrary specific value (ΔPs) of an arteriovenous pressure difference (ΔP(a-v)) by using a linear relationship between a maximum hyperemia coronary blood flow (Q(hyp.)) and the arteriovenous pressure difference (ΔP(a-v)).

This invention discloses an automated method for identifying and quantifying multi-indicator coronary artery stenosis, relating to the field of image recognition. It acquires multi-center coronary angiography image data and preprocesses it to obtain coronary angiography sequences. A keyframe selection algorithm is used to filter target frames from the coronary angiography sequences. The MDP-UNet semantic segmentation model is used to segment the target frames, and the YOLO-CCSA model is used to locate the stenosis lesions. The segmentation results and stenosis lesion detection results are fused, and a stenosis segment quantification algorithm is used to perform multi-indicator quantitative calculation and scale calibration to obtain quantification results. Based on the quantification results, the degree of stenosis is graded and assessed. The method provided by this invention achieves fully automated analysis from keyframe selection to stenosis assessment.

The invention discloses a blade cutting balloon suitable for coronary artery stenosis, and relates to the technical field of cutting balloons. Comprising an air pump, a pressure indicating gauge is installed on the outer wall of the end of the air pump, a connecting piece is arranged at the end of the air pump, a catheter is connected to the other end of the connecting piece, a balloon body is fixedly connected to the end, away from the connecting piece, of the catheter, and multiple sets of cutting blades extending to the outside of the balloon body are slidably connected to the inner wall of the balloon body. The inner wall of the balloon body is fixedly connected with a fixed tube, and a movable block is arranged on the inner wall of the fixed tube. By means of continuous pressurization of the air pump, air can enter the cavity of the fixing tube, the extension rod is driven to drive the cutting blade to stretch out in the cutting direction, then targeted deep cutting pressure can be applied to calcified tissue in a blood vessel, the problem of damage caused by secondary cutting operation is avoided, and the cutting efficiency is improved. And the repeated cutting frequency of the cutting blade is reduced, and the efficiency and effect of cutting the calcified tissue are improved to a certain extent.

This invention discloses a gene delivery vector, highly shear stress-sensitive nucleic acid nanoaggregates, their preparation methods, and applications. The highly shear stress-sensitive gene delivery vector includes either polymer PLGA-PGEA or polymer PLGA-PGEA / TA. This invention utilizes ethanolamine and amino-modified tannic acid to functionalize polymer PLGA-PGEA, constructing polymer PLGA-PGEA and PLGA-PGEA / TA gene delivery vectors, exhibiting high cell transfection efficiency, low cytotoxicity, and suitable particle size, showing promise for application in gene therapy for cardiovascular diseases. The PLGA-PGEA / TA gene delivery vector of this invention uses PLGA as a hydrophobic core to stabilize nanoparticles, while the outer PGEA / TA layer improves its water solubility and can also complex genes, obtaining nucleic acid nanoaggregates with the required particle size. These not only safely accumulate in the stenotic region of coronary arteries but also exhibit sensitivity to high shear stress dispersion in the stenotic region, demonstrating significant application value and clinical translational potential in the targeted treatment of atherosclerosis.

The invention discloses a coronary artery stenosis intelligent evaluation method and system based on multi-modal image fusion, and belongs to the technical field of medical image processing. According to the method, the problem that respective advantages of various modal images are difficult to consider in the prior art is solved, the importance score of each modal image in coronary artery stenosis evaluation is calculated, different weights are given according to the score, and the reasonability of weight distribution is ensured by combining introduction of a time sequence image feature library and a dynamic time window; constructing a time sequence image feature library exclusive to the patient, and setting a dynamic time window to perform principal component analysis, so that the assessment can dynamically reflect the pathological states of the patient at different time points; according to the method, the dynamic weight with the time sequence perception capability is generated by identifying the rising and falling trend of the contribution degree of the image data of each mode and endowing the trend with the reward weight, so that the evaluation result is more suitable for the real-time illness state of the patient, and a more accurate, comprehensive and dynamic diagnosis basis is provided for clinic.

The application provides a kind of coronary artery detection method and system fusing FSRConv and Transformer in the technical field of medical image processing, method includes following steps: step S1, a large number of historical CT angiography images of coronary artery are acquired, and a data set is constructed after each historical CT angiography image is preprocessed;Step S2, a coronary stenosis detection model is created based on a 3D feature space reconstruction module, a self-attention module and an output module;Step S3, the coronary stenosis detection model is trained through the data set;Step S4, the real-time CT angiography image of coronary artery is detected by the trained coronary stenosis detection model.The advantage of the application lies in: greatly improve the accuracy of coronary artery stenosis detection.

The present disclosure relates to a method for providing information necessary for assessing severity of coronary artery stenosis, the method includes: administering a contrast agent to a coronary artery; capturing an angiographic image; setting a suspected stenosis area where stenosis is suspected and a proximal area where blood passes before the suspected stenosis area within region of captured image for observing the coronary artery stenosis based on the captured angiographic image; fixing position of the coronary artery where the contrast agent is administered by image processing; and deriving a blood flow velocity ratio, which is a relative ratio of blood flow in the proximal area and the suspected stenosis area, based on time when brightness changes in the proximal area and the suspected stenosis area of the captured image.

The application discloses a photon CT low-dose low-concentration contrast agent scheme coronary stenosis evaluation system and belongs to the medical image field.The photon CT low-dose low-concentration contrast agent scheme coronary stenosis evaluation system comprises a multi-energy spectrum data acquisition unit, a data preprocessing unit, an image generation unit, an image segmentation unit, a plaque analysis unit and a stenosis detection unit.The application solves the problems of high radiation dose, large amount of contrast agent and low efficiency and strong subjectivity caused by high dependence on the experience of doctors in the existing coronary CT examination, realizes high-quality coronary imaging and accurate stenosis evaluation under the condition of low dose and low-concentration contrast agent by adopting a photon counting CT detector and optimized low-dose scanning parameters, combining a deep learning algorithm and a multi-material decomposition technology, significantly reduces the radiation exposure and contrast agent related risk of patients, and improves the accuracy and reliability of coronary stenosis detection and plaque component analysis.

The present application relates to a kind of coronary CT image criminal vessel detection system and method based on multiscale feature extraction, belong to image processing technical field, including data preprocessing module, multiscale coronary stenosis identification module, criminal evaluation module, visualization module.Combined with multiscale coronary stenosis identification module and criminal evaluation module, multiscale image generation, feature extraction and fusion, can capture the full-scale structure from coronary trunk to small branch, through the accurate identification of coronary stenosis and its criminal evaluation realizes the coronary criminal vessel detection based on coronary CT image.Through identification and logistic regression evaluation, aims at improving the accuracy of coronary criminal vessel detection.