76 results about "Lung embolism" patented technology

A method of visualizing structural information of pulmonary vessels in medical images includes obtaining structural information of pulmonary vessels based on anatomical structure features of the pulmonary vessels from a medical image based on segmentation results of a pulmonary vascular region of the medical image; obtaining pulmonary embolism finding information in the pulmonary vessels of the medical image; visualizing the structural information of the pulmonary vessels in accordance with hierarchical information; and visualizing the pulmonary embolism finding information in association with individual sub-regions of the structural information of the pulmonary vessels visualized in accordance with the hierarchical information.

The invention discloses a multi-center child pulmonary embolism online data platform system, and belongs to the technical field of data processing. A data collection step: collecting electronic medical records, image data, laboratory results and treatment scheme multi-source data uploaded by each center, and unifying data formats and standardizing processing flows; a machine learning and artificial intelligence algorithm step: carrying out deep analysis on mass data, identifying risk factors, pathogenesis and treatment response modes of children pulmonary embolism, and supporting personalized medical decisions; a dynamic risk assessment tool step: developing a dynamic risk assessment tool according to the latest research results and clinical guidelines, and providing real-time treatment suggestions and prognosis assessment for doctors; a data security and privacy protection step: adopting a data encryption step and an anonymization step to ensure the security and privacy protection of patient information; and a multi-center cooperative network step: establishing a children pulmonary embolism research and treatment cooperative network nationwide through a data sharing and remote cooperation mechanism.

The invention relates to the technical field of heterogeneous cross-modal fusion, and discloses a pulmonary embolism auxiliary diagnosis method and system based on heterogeneous cross-modal fusion, and the system comprises the following modules: an image module which combines Swin Transform and an improved 3D CNN model to extract features; the polarization self-attention module is used for efficiently processing high-resolution 3D input features and reducing calculation overhead; the multi-scale feature fusion module integrates SE module channel feature extraction, ASPP module multi-scale feature extraction and 3D convolution space feature extraction; the EHR module is used for extracting features from the electronic health records; and the fusion module is composed of a multi-modal fusion module MAFM and is responsible for extracting key features from the image and the EHR module and fusing the key features so as to improve the diagnosis effect. Through a parallel network and a multi-modal fusion mechanism, the diagnosis precision and robustness of pulmonary embolism are remarkably improved.

The invention discloses a CT (Computed Tomography) image pulmonary embolism segmentation and classification method combined with quality evaluation, which relates to the technical field of image processing, and comprises the following steps: inputting a 256 * 256 pulmonary embolism CT image and a quality score thereof into a quality score guide encoder, expanding a quality score dimension through linear transformation, and carrying out point product fusion with a feature map extracted by ResNet34 layer by layer to obtain a final product; generating multi-scale coding features; performing wavelet domain decomposition and reconstruction on the coding features through a wavelet transform fusion jump link module, and optimizing feature transmission; a multi-scale cross enhanced decoder is adopted to carry out multi-scale deconvolution fusion on the features, a segmentation result is output in combination with an efficient channel attention mechanism, meanwhile, pulmonary embolism existence judgment is output through a classification head, and the method provides powerful support for early diagnosis of pulmonary embolism, development of an image auxiliary diagnosis system and clinical application, and has good application prospects. Wide application prospects and profound social significance are realized.

The invention relates to the technical field of medical information processing, and discloses an acute pulmonary embolism auxiliary diagnosis decision-making system based on a large model, and the system comprises a data collection and preprocessing module which is used for receiving multi-source clinical data and preprocessing the multi-source clinical data; the large model reasoning module is used for inputting the preprocessed multi-source clinical data into a large model fused with a multi-modal causal attention and identification adversarial learning mechanism, and outputting a reasoning result; the dynamic learning module is used for accessing desensitized case data of a hospital electronic medical record system, continuously learning new cases through federal learning, and optimizing the recognition capability of a large model pair; the diagnosis suggestion generation module is used for generating structured diagnosis suggestions according to the reasoning result output by the large model; the result output and interaction module is used for displaying diagnosis suggestions to doctors and updating reasoning results in real time by a large model when the doctors supplement data; the diagnosis efficiency is improved.

The invention belongs to the field of pulmonary embolism detection and segmentation, and discloses a residual error jump enhancement and multi-dimensional interactive attention combined network structure, which comprises an encoder, a decoder, a jump connection fusion module and a bottleneck module which are respectively responsible for feature extraction, spatial restoration, multilayer semantic fusion and modeling of a long-distance dependency relationship. The AGRS module is fused with a residual jump enhancement structure and a multi-dimensional interaction quadruple attention module; the CDB-ASPP module realizes multi-scale context information efficient fusion through five parallel convolution branches with different expansion rates, combines a global-local enhancement strategy, and considers both a global receptive field and a local detail perception capability; and a dual attention Transform module is used for enhancing the global feature modeling capability. According to the method, an effective solution is provided for solving key problems such as semantic gaps, context information missing and small target detection sensitivity, and important reference and enlightenment are provided for deep learning-based pulmonary embolism auxiliary diagnosis research.

A method of assessing a likelihood of a patient having a pulmonary embolism (PE) comprises receiving discrete patient data, including patient-related clinical and demographic data pertinent to the patient, receiving electrocardiograph (ECG) waveform data obtained from examination of the patient, processing both the received discrete patient data and received set of ECG waveform data using a supervised deep learning multimodal fusion model that has been trained using analogous input training data including both discrete patient data and ECG waveform data to obtain an optimal match to results from corresponding patient computed tomography pulmonary angiograms (CTPA), indicative of a presence or absence of a pulmonary embolism, and outputting a measure of the likelihood of the patient having a pulmonary embolism.

The invention relates to the technical field of biology, in particular to nucleic acid targeting a blood coagulation factor XI and application of the nucleic acid. The nucleic acid provided by the invention can effectively reduce the blood coagulation factor XI level, and can effectively inhibit the formation of thrombus, so that various diseases caused by thrombus, such as pulmonary embolism, venous embolism, deep vein embolism, myocardial infarction, stroke and the like, can be prevented and / or treated.

The present invention encompasses the use of general chymotrypsin inhibitors, and more specifically, substituted bicyclic substituted uracil of general formula (I) as described and defined herein, and 3-methylbenzo-[b] thiophene)-2-sulfonamido derivatives of general formula (II) for the manufacture of pharmaceutical compositions for the treatment or prevention of stroke, pulmonary embolism, pulmonary embolism, and the like. The traditional Chinese medicine composition can be used for treating thrombosis, deep vein or superficial vein thrombosis, thrombotic microangiopathy, infection, inflammation, thrombotic microangiopathy in a hypercoagulable state after transplantation, disseminated intravascular coagulation, vaccine-induced immune thrombotic thrombocytopenia, and thrombosis or occlusion at a vascular access part.

This invention discloses a method, device, electronic equipment, and medium for assessing the risk of pulmonary embolism. It relies entirely on collected non-invasive objective data and eliminates subjective judgments based on medical history or physician experience, effectively avoiding risk misjudgments caused by subjective variables in the PESI / sPESI model. This ensures the consistency and reliability of the assessment results. The ratio of Qa to Qb is used as the core assessment indicator. This ratio is a continuous variable, eliminating the need for empirical segmentation of parameters and avoiding significant differences in assessment results among patients with similar conditions. The risk level is assessed based on the ratio of Qa to Qb obtained through hemodynamic simulation. The results perfectly match clinical understanding of the severity of pulmonary embolism, improving assessment accuracy. Simultaneously, it reduces patient examination trauma and avoids high equipment and operational costs.

Quinazoline dione compounds are provided for treating cardiac indications such as hypertrophic cardiomyopathy and diastolic dysfunction. Diseases treated by the methods described herein include, but are not limited to, cardiomyopathy, hypertrophic cardiomyopathy (HCM), abnormal heart rhythms, aortic disease, Marfan syndrome, coronary artery disease, heart attack, heart failure, rheumatic heart disease, peripheral vascular disease, stroke, deep vein thrombosis, and pulmonary embolism.

The invention relates to a pulmonary embolism image segmentation method and device based on a deep residual network, and relates to the technical field of deep learning. The method comprises the following steps: inputting a training set image with a pulmonary embolism labeling area into a preset deep residual network, and extracting multi-layer image features of the training set image; performing feature fusion on the multi-layer image features to generate a segmented image with a pulmonary embolism prediction area; decoding and reconstructing the multi-layer image features based on an attention guidance mechanism, and generating a multi-layer attention map with a pulmonary embolism prediction area; constructing a comprehensive loss function according to the segmented image, the multi-layer attention map and the training set image, and optimizing model parameters of the deep residual network based on the comprehensive loss function; and performing pulmonary embolism region segmentation labeling on the to-be-segmented image by using the optimized deep residual network. By adopting the method, the feature expression capability and the small target recognition capability of the pulmonary embolism image can be improved.

A vacuum aspiration system may be used to treat thromboembolic disease, such as deep vein thrombosis or pulmonary embolism. The system includes a housing, and a fluid flow path extending through the housing. An aspiration catheter is in fluid communication with the flow path, and a connector is configured to place a source of aspiration in communication with the flow path. A hemostasis valve is provided in the housing. The housing may be configured to receive and releasably engage a thrombus engagement tool.

A patient screening system for providing recommendations for screening of a hospitalized patient for risk of developing deep vein thrombosis (DVT) or pulmonary embolism (PE) based on their health records and retinal images, is described herein. The patient screening system may include an optical imaging device for capturing retinal images, and a DVT risk assessment system configured to generate the recommendation for further screening tests. The DVT risk assessment system may implement various AI / ML models trained on a training dataset of anonymized patient data. The patient screening system may also implement detectors for various ophthalmic features correlated with blood clot-related conditions of the patient. Any patient screening based on the recommendation may be followed up, and results of such screening used to improve performance of the patient screening system.

The present invention relates to the technical field of biology, and in particular to a nucleic acid targeting coagulation factor XI and a use thereof. The nucleic acid of the present invention can effectively reduce the level of coagulation factor XI and can effectively inhibit the formation of thrombi, thereby preventing and / or treating multiple diseases caused by thrombi, such as pulmonary embolism, venous embolism, deep venous embolism, myocardial infarction, and stroke.

The present invention relates to the technical field of medical devices, and discloses a filter assembly and a thrombus aspiration assembly for removing pulmonary embolism thrombi. The filter assembly comprises: a thrombus crushing filter that can be radially elastically expanded and coaxially arranged, a first isolation filter and a second isolation filter; the thrombus crushing filter is arranged between the first isolation filter and the second isolation filter. The thrombus aspiration assembly comprises: a thrombus aspiration tube, a thrombus output tube, a thrombus aspirator and a connecting seat with a hollow channel; the hollow channel is used to pass through the filter assembly as described above after being stored; the thrombus aspiration tube is provided at the front end of the connecting seat, and a flexible sealing valve is provided at the rear end opening of the connecting seat, the inner end of the thrombus output tube is connected to the side of the connecting seat, and the outer end of the thrombus output tube is connected to the thrombus aspirator. This embodiment can effectively remove large pulmonary embolism thrombi, reduce the complexity of surgery, and shorten the operation time.

The present invention provides a method for identifying pulmonary embolism based on non-contrast CT, belonging to the technical field of deep learning. In order to solve the problem in the prior art that the identification of pulmonary embolism mostly relies on contrast-enhanced CT and it is impossible to quickly and accurately identify pulmonary embolism for non-contrast CT data. The method of the present invention performs pulmonary contour segmentation on non-contrast CT images through a pulmonary contour segmentation model; performs segmentation of pulmonary blood vessels in non-contrast CT through a blood vessel segmentation model; through a pulmonary embolism extraction model, uses the pulmonary embolism area on contrast-enhanced CT as the gold standard to mark the pulmonary embolism area in non-contrast CT, determines the embolism suspicious area by considering the CT value difference between pulmonary embolism and blood vessels and the vascular morphological features caused by pulmonary embolism for the pulmonary embolism in non-contrast CT, optimizes the model by minimizing the difference between the suspicious area and the gold standard, and uses the optimized model to perform pulmonary embolism segmentation on non-contrast CT. The present invention can achieve high sensitivity and accuracy in predicting pulmonary embolism based on non-contrast CT.

The invention discloses a pulmonary embolism patient thrombolysis treatment bleeding and death risk fusion prediction method, which is used for effectively evaluating the bleeding and death risk of a pulmonary embolism patient after thrombolysis treatment and assisting a doctor to carry out individualized evaluation on the bleeding risk of the patient so as to formulate a most suitable treatment plan and improve the treatment accuracy. And the treatment safety of the patient is further ensured. According to the method, an efficient tree model integration algorithm XGBoost is adopted, meanwhile, EHR data of a pulmonary embolism patient receiving thrombolysis treatment is used for training and evaluating a machine learning model, the risk estimation result of bleeding and death of the pulmonary embolism patient after thrombolysis treatment can be effectively obtained in combination with evaluation indexes, the risk estimation result is used for assisting doctors in making clinical decisions, and the risk estimation accuracy is improved. The treatment accuracy is improved, and the treatment safety of a patient is guaranteed.

The application discloses a CT image pulmonary embolism segmentation and classification method combined with quality evaluation, relates to the technical field of image processing, and comprises the following steps: inputting a 256*256 pulmonary embolism CT image and a quality score thereof into a quality score guided encoder, expanding the quality score dimension through linear transformation, point multiplying and fusing with feature maps extracted by ResNet34 layer by layer, and generating multi-scale coding features; carrying out wavelet domain decomposition and reconstruction on the coding features through a wavelet transform fusion skip link module, and optimizing feature transmission; adopting a multi-scale cross enhancement decoder to perform multi-scale deconvolution fusion on the features, outputting a segmentation result in combination with an efficient channel attention mechanism, and outputting a pulmonary embolism existence judgment through a classification head, so that the method provides strong support for early diagnosis of pulmonary embolism, development of an image assisted diagnosis system and clinical application, and has wide application prospect and far-reaching social significance.

The invention relates to the technical field of risk prediction of diseases, in particular to a pulmonary embolism risk layering method and system and a storage medium. The pulmonary embolism risk layering method comprises the following steps: inputting a CTPA image into an image segmentation model to obtain a segmented image which is output by the image segmentation model and marked with a focus area; and inputting the segmented image into a prediction model to obtain a pulmonary embolism risk hierarchical prediction result output by the prediction model, the pulmonary embolism risk hierarchical prediction result being determined by the prediction model according to the image omics characteristics of the focus area, and the image omics features are extracted from the lesion area by the prediction model.

The invention discloses a pulmonary embolism risk prediction model for a critical community-acquired pneumonia patient and an establishment method. The method comprises the following steps: 1, inputting patient data; 2, sorting and cleaning data, especially processing missing values and abnormal values; 3, randomly splitting the data into a training set and a verification set in proportion, and performing population and baseline feature relationship analysis on the split data set; 4, the LASSO regression and the multi-factor logic regression are adopted to determine the independent risk factors of the intensive care unit-intensive community acquired pneumonia, the ICU-SCAP patient pulmonary embolism and the PE, and a risk prediction model is constructed on the basis of the training set; and 5, performing internal verification on the risk prediction model based on the verification set, and evaluating the distinction degree, the calibration degree and the clinical benefit of the risk prediction model. Based on the data processing strategy of the system, in combination with advanced modeling methods such as LASSO, GAM, RCS and the like, the PE risk of the ICU-SCAP patient is accurately evaluated, and the method has good technical advancement, stability and clinical practical value.

The invention discloses a pulmonary embolism detection system combining CT pulmonary arteriography and a neural network, and relates to the technical field of angiography image processing. Comprising five parts, namely CT pulmonary arteriography data collection, data set annotation and division, CT pulmonary arteriography image enhancement, data set amplification and data normalization, and is used for processing a patient image, wherein the CT pulmonary arteriography sequence image cleaning and enhancement network is composed of five parts, namely CT pulmonary arteriography data collection, data set annotation and division, CT pulmonary arteriography image enhancement, data set amplification and data normalization; the CT pulmonary arteriography multi-scale feature extraction network is composed of a residual transformation network and a bidirectional feature pyramid network and is used for extracting data features; the pulmonary embolus potential position detection network is composed of an independent self-attention mechanism and is used for detecting the position area of pulmonary embolus; the pulmonary embolism type identification and positioning network is composed of two identification modules connected in series and is used for pulmonary embolism type judgment and position positioning.

The present application provides a method and apparatus for processing lung medical images, a storage medium and a computer device, the method comprising: performing segmentation processing on the lung medical image to determine the detection region of interest image; inputting the detection region of interest image into the pulmonary embolism detection model to determine the embolism region in the detection region of interest image; determining the lung structure region of interest in the detection region of interest image, the lung structure region of interest being associated with lung anatomical structure information; performing embolism positioning processing on the lung structure region of interest according to the embolism region and lung anatomical structure information to determine the structural position information of the embolism region in the lung structure region of interest; and associating and displaying the embolism region and the structural position information in the lung medical image. The above method can intuitively obtain the detailed coordinates and morphological data of the lung anatomical structure where the pulmonary embolism is located, while taking into account the accuracy and efficiency of pulmonary embolism detection.

The present invention covers the use of chymase inhibitors in general and more in particular substituted bicyclically substituted uracils of general formula (I) as described and defined herein, and 3-methylbenzo-[b]thiophene)-2-sulfonamido derivatives of general formula (II) for manufacturing pharmaceutical compositions for the treatment or prophylaxis of stroke, pulmonary embolism, deep or superficial vein thrombosis, thrombotic microangiopathy, thrombotic microangiopathy in hypercoagulable states after infection, inflammation, transplantation, disseminated intravascular coagulation, vaccine-induced immune thrombotic thrombocytopenia, vascular access site thrombosis or occlusion.

The invention provides a pulmonary embolism detection and segmentation method based on intermediate distance compensation, and relates to the technical field of computer medical image processing, and the method comprises the steps: carrying out the pre-labeling and preprocessing of a lung CT image set, and obtaining a pre-labeling label and a standard image set; obtaining a thrombus label through the pre-labeling label; integrating the thrombus label and the standard image set, and inputting the integrated thrombus label and the integrated standard image set into a three-dimensional target detection network model for training to obtain a detection training model; performing detection and identification through the plurality of detection training models to obtain a plurality of preliminary detection results, and fusing the preliminary detection results into an identification result; the thrombus label, the standard image set and the lung CT image set are integrated and then input into a semi-supervised semantic segmentation model for model training, and a final segmentation model is obtained; and performing segmentation correction on the identification result through the final segmentation model and an intermediate distance compensation mechanism to obtain a combination result. According to the method, the pulmonary embolism is intelligently recognized through deep learning and image processing, and the detection accuracy of the pulmonary embolism is improved.

The invention discloses a pulmonary embolism adverse event risk assessment method, device and equipment and a storage medium, a functional pulmonary embolism adverse event risk assessment model is constructed, three objective parameters of age, heart rate and blood oxygen partial pressure are directly incorporated into the model, weights of the age, the heart rate and the blood oxygen partial pressure are obtained through objective derivation (non-manual setting), and the risk assessment model is used for evaluating the risk of the pulmonary embolism adverse event. According to the method, the dependence on doctor experience is thoroughly eliminated, assessment deviation caused by subjective factors is avoided, the functional risk assessment model has continuity, and the problem that the assessment results of two patients with similar parameters are greatly different in the prior art is avoided.

The present invention relates to the field of medical device technology, and discloses a basket-type thrombus removal device. The device comprises: a distal filter disc, a proximal recovery disc and at least one intermediate thrombus removal disc that can be radially elastically expanded and have a mesh structure; the distal filter disc, the intermediate thrombus removal disc and the proximal recovery disc are coaxially connected in sequence; in a naturally unfolded state, the intermediate thrombus removal disc is in the shape of a flat disc, and the diameter of the intermediate thrombus removal disc is greater than the diameter of the distal filter disc. The embodiment of the present invention can provide a fast, safe and effective thrombus removal method for the treatment of embolism with large size and large amount of thrombus, such as pulmonary embolism, thereby helping to improve the treatment effect of patients with acute medium and high-risk pulmonary embolism.

The invention relates to the technical field of medical treatment. The lantern-shaped pulmonary embolism thrombus cleaning device comprises a driving cylinder, a suction cylinder arranged at the lower end of the driving cylinder and communicated with the driving cylinder, and a thrombus removing pipe capable of being placed in the thrombus position through a catheter. A suction piston which is matched with an inner cavity of the suction cylinder and moves in the vertical direction is arranged in the suction cylinder. A driving rod is arranged in the driving cylinder, and the lower end of the driving rod extends into the suction cylinder and is connected with the suction piston; the upper end of the driving cylinder is provided with a cylinder cover with a spiral hole in the center, the upper section of the driving rod is provided with a spiral section matched with the spiral hole, the spiral section of the driving rod is arranged in the spiral hole in a penetrating mode, and the upper end of the driving rod is connected with the tail plate through a rotating connector; the thrombus removing device can conduct rotary crushing cutting and suction on thrombus at the same time, the thrombus removing efficiency is greatly improved, and meanwhile the thrombus removing device is simple in structure and good in stability.

An apparatus and methods for use are provided, where the apparatus includes: (a) a first catheter having a proximal end and a distal end, and wherein a distal portion of the first catheter includes a first one or more outlets, (b) a first tubular housing having a proximal end and a distal end, wherein the first tubular housing is coupled to the first catheter proximal to the at least one first outlet, (c) one or more pressure sensors coupled to the distal end of the first tubular housing, and (d) a second catheter having a proximal end and a distal end, wherein a distal portion of the second catheter includes a second one or more outlets, and wherein the distal end of the second catheter is configured to be positioned substantially within one of (i) the first catheter or (ii) a second tubular housing coupled to one or more of the first catheter and the first tubular housing, when the second catheter is in a first position.

The present invention relates to a plant composition, a traditional Chinese medicine composition and use thereof. The plant composition comprises Prepared Monkshood Daughter Root (Aconitum carmichaelii), Fragrant Solomonseal Rhizome (Polygonatum odoratum), Indian Bread (Poria cocos), Pinellia tuber (Pinellia ternata), Oriental Wormwood Herb (Artemisia scoparia), Scutellaria Root (Scutellaria baicalensis), Mongolian Snakegourd Fruit (Trichosanthes kirilowii), Magnolia Bark (Magnolia officinalis), Heartleaf Houttuynia Herb (Houttuynia cordata) and Baked Licorice Root and Rhizome (Glycyrrhiza glabra), which is used as a traditional Chinese medicine composition. In addition, the traditional Chinese medicine composition can be used to treat pulmonary embolism or pulmonary fibrosis.