42 results about "Left cardiac chamber" patented technology

The embodiment of the invention relates to a processing method and device for predicting ejection fraction retention type heart failure. The method comprises the steps that a deep learning model used for conducting dichotomy prediction on the ejection fraction retention type heart failure is constructed and recorded as a first prediction model; constructing a model data set and recording the model data set as a first data set; training the first prediction model based on the first data set; after model training is finished, heart structure feature analysis, heart hemodynamic feature analysis and left ventricle vortex time sequence feature analysis are conducted on four-dimensional blood flow heart magnetic resonance imaging data input by a user to obtain a corresponding structure feature set, and the hemodynamic feature set and the vortex time sequence feature form a corresponding first feature data set; and inputting the first feature data set into a first prediction model for prediction to obtain a corresponding first classification vector which is fed back to the current user. According to the invention, the prediction accuracy and the prediction efficiency can be improved.

An implantable LVAD is provided that includes a pump shaped so as to define a pump chamber. The pump includes a tubular linear motor, which includes a magnetic piston, which includes a reciprocating one-way valve configured to allow downstream blood flow and inhibit upstream blood flow; and a stator, which is configured to magnetically drive the magnetic piston with reciprocating motion, so as to pump blood downstream during downstream motion of the magnetic piston while the reciprocating one-way valve is closed. The pump further includes a spring, which is arranged to store energy during upstream motion of the magnetic piston and release the stored energy during the downstream motion of the magnetic piston. Control circuitry is configured to activate the tubular linear motor to provide pulsatile flow synchronized with cardiac cycles. Other configurations are also described.

An implantable LVAD system includes an implantable LVAD including an outflow cannula, which is couplable in fluid communication with a circulatory system of the patient at a first site; and an inflow cannula, which is couplable in fluid communication with the circulatory system at a second site upstream of the first site. The LVAD further includes a continuous-flow pump includes a first inlet in fluid communication with the inflow cannula, and a first outlet. The LVAD still further includes a pulsatile-flow pump includes a second inlet in fluid communication with the first outlet of the continuous-flow pump, and a second outlet in fluid communication with the outflow cannula. Control circuitry is configured to activate the continuous-flow pump to provide flow without synchronization with cardiac cycles of a heart of the patient, and activate the pulsatile-flow pump to provide pulsatile flow synchronized with the cardiac cycles. Other embodiments are also described.

The utility model discloses a ventricular simulation device and an extracorporeal circulation simulation system, the ventricular simulation device comprises a first simulation chamber and a second simulation chamber arranged in the first simulation chamber, and a first medium cavity is formed between the first simulation chamber and the second simulation chamber. The first simulation chamber is provided with a medium circulation opening communicated with the first medium cavity so as to change the medium pressure in the first medium cavity. A second medium cavity is formed in the second simulation chamber, and when the medium pressure in the first medium cavity is larger than the sum of the medium pressure in the second medium cavity and the minimum pressure needed by deformation of the second simulation chamber, the second simulation chamber shrinks and discharges the medium; and when the sum of the medium pressure in the first medium cavity and the minimum pressure required for deformation of the second simulation chamber is smaller than the medium pressure in the second medium cavity, the second simulation chamber expands, and the medium enters the second medium cavity. The contraction and relaxation of the second simulation chamber are realized by changing the compression state of the second simulation chamber, so that the contraction and relaxation of the left ventricle are accurately simulated, various heart failure states of a human body are accurately simulated, and the test is convenient.

In some instances, a computing device can determine information corresponding to a data structure and indicating delays associated with atrial leads, left ventricular (LV) leads, and right ventricular (RV) leads, based on one or more input variables. The computing device can determine multiple personalized characteristics based on the information corresponding to the data structure. The computing device can receive multiple second sets of electrical measurements from multiple measuring electrodes, based on the multiple personalized characteristics, indicating a second electrical signal applied to the patient's heart. The computing device can use a first set of electrical measurements (e.g., raw measurements) and the multiple second sets of electrical measurements to determine a cardiac resynchronization index (CRI) value. The computing device can generate a graphical representation based on the populated data structure and display the graphical representation.

The invention discloses a myocardial amyloidosis quantitative analysis method based on SPECT imaging and related equipment, and relates to the field of wisdom medical device.The method comprises the steps that firstly, three-dimensional SPECT original data of N time phases of a target patient in the cardiac cycle are collected, a mu graph is generated based on low-dose CT for registration and attenuation correction, and a dynamic image is reconstructed; and then an MR structure sequence under synchronous electrocardio gating is obtained, and SPECT and MR space standardization and partial volume effect correction processing are completed. And unifying the cardiac cycle image form through affine and nonlinear transformation, and generating a high-quality enhanced SPECT image. Finally, radioactive counting information is extracted on the basis of three-dimensional segmentation, multiple myocardial quantitative indexes are calculated, comprehensive analysis of myocardial functions is achieved in combination with left ventricular functional parameters, and accurate image support is provided for non-invasive diagnosis and curative effect evaluation of myocardial amyloidosis.

The application provides a cardiac phase judgment device, method, medium and electronic equipment. The device comprises: a medical image acquisition module, configured to acquire a plurality of three-dimensional medical images of a target object in at least one cardiac cycle; a myocardial wall thickness acquisition module, configured to acquire the thickness of a left ventricular myocardial wall in each frame of the three-dimensional medical images; and a cardiac phase judgment module, configured to acquire the cardiac phase of the target object according to the thickness of the left ventricular myocardial wall in each frame of the three-dimensional medical images. The cardiac phase judgment device only needs to acquire the cardiac phase of the target object according to the three-dimensional medical images of the target object, and is simpler to implement.

The invention discloses an ultrasonic image processing method. The method comprises the steps that a time-phase section recognition model is acquired; the time phase section recognition model comprises a left ventricle segmentation unit; inputting the cardiac ultrasound video into a time-phase section recognition model to trigger the time-phase section recognition model to obtain a heart standard section corresponding to at least one time phase based on the detected image features and the left ventricle segmentation result; processing the heart standard section under each time phase by using the image segmentation model to obtain a segmentation result; the segmentation result comprises point-line features of each target tissue in the heart standard section; and determining the detection result of the heart standard section in each time phase in parallel according to the point-line characteristics of each target tissue. By applying the technical scheme provided by the invention, the heart standard section can be measured more accurately and efficiently. The invention also discloses an ultrasonic image processing device, electronic equipment and a computer readable storage medium, which also have the above technical effects.

A magnetic driver generates a magnetic field that rotates about an axis and induces nutation in a disc with an array of magnets (wobble plate). The magnetic driver can be a drive disc with an array of magnets and rotated by a drive motor, or can be electromagnetic coils sequentially energized, to provide the rotating magnetic field. The wobble plate nutates about the axis of the rotating magnetic field but is constrained from rotating about the axis. The wobble plate can be enclosed in a pump housing where nutation of the wobble plate causes pumping of a fluid to occur, such as in an implanted Left Ventricular Assist Device (LVAD) or a Total Artificial Heart (TAH). Driven in reverse, an input pumping fluid may induce nutation of the wobble plate which in turn induces rotary motion of a drive disc that drives a generator or a motor / generator.

A method (100) for classifying a patient's diastolic function, comprising: (i) receiving (120), from an ultrasound device (280), a plurality of 2D echocardiographic images of the patient's heart; (ii) analyzing (150), by a trained diastolic function prediction algorithm, the plurality of 2D echocardiographic images of the patient's heart to estimate left ventricular end-diastolic pressure (LVEDP); (iii) classifying (160) the patient's diastolic function as normal or abnormal based on the estimated LVEDP; and (iv) providing (170), to a user via a user interface, an indication of the patient's diastolic function as normal or abnormal.

This invention discloses an automatic identification and classification method for scarred myocardium based on cine magnetic resonance imaging (CMRI) and deep learning. After preprocessing cardiac cMRI images, the method extracts the relative motion and imaging features of the left ventricular myocardium. A multimodal feature fusion network is then used for training and prediction to identify and classify five different types of scarred myocardium. The extraction of relative motion and imaging features enables an accurate and detailed description of the state of scarred myocardium. The multimodal feature fusion network model captures the compensatory effects of long-distance scarred myocardium movement, the non-rigid deformation motion patterns of the myocardium, and the spatiotemporal relationship between myocardial motion and morphology. This invention achieves high efficiency and accuracy in identifying and classifying scarred myocardium, saving doctors time and effort in diagnosing cardiovascular diseases leading to scarred myocardium, reducing patient suffering, and facilitating a more stable and simpler examination and diagnostic method.

The invention discloses a cardiac ultrasound sternum-side short-axis section scanning method and device for an ultrasonic robot, and belongs to the technical field of medical ultrasonic robot control. The method comprises the following steps: S1, calculating the cardiac cycle phase of the heart; and S2, performing short-axis section scanning based on the left ventricle short-axis profile. The invention relates to an automatic scanning method for phase identification of composite complex motion combining short-axis scanning beside sternum with heartbeat cycle. The heart ultrasonic scanning is carried out through the method, and a heart section image which is good in repeatability, high in efficiency, stable in section quality and continuous in physical space can be obtained. The method is suitable for improving the imaging quality in the heart scanning process of the autonomous ultrasonic robot. The specific method of the ultrasonic robot in the field of short-axis section subdivision beside the heart is provided for the first time, and the method has good application prospects.

The invention relates to a mitral valve outer valve frame, a mitral valve frame assembly and a heart valve prosthesis. The invention relates to a ventricular atrial cavity which has a radial contraction state and a radial expansion state and comprises an atrial part, a ventricular part and a connecting frame, the atrial part and the ventricular part are connected through the connecting part, and the atrial part and the ventricular part are of a latticed structure and comprise frame units; the ventricular part comprises at least three protruding parts which are distributed at intervals in the circumferential direction, an avoiding space is formed between every two adjacent protruding parts, the area of the avoiding space is gradually increased in the direction from the inflow end to the outflow end, and due to the fact that an avoiding area formed between every two adjacent supporting pieces has small influence on the ventricular autologous structure, the ventricular autologous structure can be effectively improved. The avoiding area is aligned with the left ventricular outflow tract during anchoring, so that the problem that the left ventricular outflow tract is blocked due to deformation after the mitral valve outer valve frame is implanted is solved.

VfA cardiac therapy uses an implantable medical device or system. The implantable medical device includes a tissue-piercing electrode implanted in the basal and / or septal region of the left ventricular myocardium of the patient's heart from the triangle of Koch region of the right atrium through the right atrial endocardium and central fibrous body. The device may include a right atrial electrode, a right atrial motion detector, or both. The device may be implanted completely within the patient's heart or may use one or more leads to implant electrodes in the patient's heart. The device may be used to provide cardiac therapy, including single or multiple chamber pacing, atrioventricular synchronous pacing, asynchronous pacing, triggered pacing, cardiac resynchronization pacing, or tachycardia-related therapy. A separate medical device may be used to provide some functionality for cardiac therapy, such as sensing, pacing, or shock therapy.

A transcatheter septal myotomy device includes a shaft (120) including a first lumen and a channel formed in a sidewall thereof, and an electrosurgical wire (150) slidably disposed through the first lumen of the shaft. A distal end (154) of the electrosurgical wire is coupled to a distal end (126) of the shaft. The electrosurgical wire is configured to transform from a straightened configuration wherein the electrosurgical wire is within the first lumen and the channel to a deployed configuration wherein the electrosurgical wire is axially translated such that a distal portion of the electrosurgical wire extends out of the channel. The electrosurgical wire is configured to be energized to splay tissue of a heart.

An antibiotic wafer system to be used to reduce or eliminate post-operative infection in connection with implanting cardiac implantable electronic devices such as implantable defibrillators, permanent pacemakers, left ventricular assist devices. The wafers will be simpler and cheaper to manufacture than prior art pouches. The wafers will slowly deliver therapeutic antibiotic levels. Unlike prior systems which are only partially biodegradable, the wafer system is completely biodegradable and resorbable wafer mesh. The surgeon will find the post-operative wound closing easier because the wafer will occupy a reduced volume in the implantation pocket and around the implanted device.

Methods and systems are provided for identifying cardiac cycles for assessing left ventricular function using only image data. In one example, a method includes obtaining (402) a sequence of medical image frames of a heart of a patient over a plurality of cardiac cycles, where each medical image frame depicts a same cardiac apex view of the heart; using a timing model to generate (406) an output that can be used to mark each frame in the sequence by a cardiac cycle sub-phase; and using the output to determine (408, 504) the suitability of each cardiac cycle for performing the automatic measurement. The method may also include using the suitability for each cycle to determine whether an automatic measurement should be performed on each cycle, where the automatic measurement is performed (412) on a medical image frame corresponding to a suitable cycle and the automatic measurement is not performed on a medical image frame corresponding to an unsuitable cycle.

The invention discloses an unsupervised domain adaptive segmentation method and system for an echocardiogram based on anatomy guidance, and belongs to the field of medical image analysis. According to the method, the problems of low segmentation precision and poor robustness of an existing unsupervised domain adaptation method in an echocardiogram segmentation task are solved. An anatomical context alignment module is provided on the image level, and a source domain image is made to adapt to an anatomical context of a target domain through two strategies of left ventricle dominant clipping and four-cavity complete mirroring; a dissection affinity refining module is provided on the feature level, the dissection affinity relation between pixels is modeled in a combined mode through space affinity regularization and time sequence affinity refining, the fine dissection consistency of domain adaptation is enhanced, and noise interference is restrained. According to the method, the segmentation precision and the cross-domain generalization ability of the segmentation network can be remarkably improved by using the dissection-guided space-time affinity unsupervised domain adaptation framework, and the robustness of the segmentation network is improved. The method can be applied to echocardiogram segmentation.

The application discloses an early evaluation system for diastolic heart dysfunction, and relates to the technical field of medical treatment. IVR and SR E ; a storage module is used for storing the normal reference ranges of SR IVR and SR E of healthy people in different age groups; an evaluation output module is used for comparing the SR IVR and SRE of the examinee with the normal reference ranges in the storage module, and outputting the early evaluation result of the diastolic heart dysfunction; the secondary screening is conducted on the hypertensive patients who cannot be diagnosed as having the diastolic heart dysfunction through the conventional ultrasound, so that the basis for early intervention is provided, and the incidence of the diastolic heart failure can be reduced.

A wearable, non-invasive, and non-intrusive hemodynamic monitoring device is disclosed for continuous monitoring of left ventricular (LV) hemodynamic waveforms and associated cardiovascular parameters. The device includes a flexible body with laterally extending electrode wings and a central rigid housing containing signal acquisition and transmission electronics. In various embodiments, the device includes either two or three wings configured for placement along the midsternal region and over the carotid arteries to enable simultaneous acquisition of electrical and mechanical signals via bioimpedance, ECG / EKG, and accelerometer sensors. Each wing incorporates tetrapolar bioimpedance electrode arrays, current injection electrodes, voltage sensing electrodes, and accelerometers to capture thoracic and cervical physiological signals necessary for estimating parameters such as left ventricular end diastolic pressure, extracellular fluid, left ventricular ejection fraction, cardiac out, stroke volume and QRS measures. The sensor signals are routed through flexible interconnects or magnetic mating connectors to the central housing. The system includes a wireless transmitter module supporting Bluetooth, Wi-Fi, or other wireless standards, a power supply and power management circuitry, and is constructed on ultra-thin, stretchable substrates composed of biocompatible flexible polymers. The modular design also includes user-serviceable magnetic connectors and optional adhesive layers for improved skin contact, thereby enabling continuous, real-time cardiovascular monitoring suitable for ambulatory and remote healthcare environments.

The invention discloses a heart left ventricle modeling image ventricle wall thickness calculation processing method, which comprises the following steps of: reconstructing a personalized three-dimensional left ventricle model based on a patient heart magnetic resonance image, solving a myocardial transmural gradient by adopting a Laplace-Dirichlet parameterization method, and dividing a ventricle wall into a plurality of layers; through a layered mapping strategy, determining a mapping point between the myocardium of the inner layer and the myocardium of the outer layer by adopting a method of combining cone space search and Laplace-Dirichlet value matching, and completing mapping between the outermost layer and the ventricular outer membrane by adopting a nearest neighbor algorithm; and finally, through layer thickness real-time accumulation and data post-processing, obtaining accurate left ventricular wall thickness distribution data. The method overcomes the defect that a rigid mapping method easily generates measurement deviation when facing pathological forms such as myocardial fibrosis and ventricular wall tumors, improves the precision and reliability of ventricular wall thickness measurement, and provides effective technical support for accurate diagnosis, treatment planning and dynamic monitoring of cardiovascular diseases.

Embodiments of the present invention provide a system for automatically deriving parameters of a human heart from ultrasound results. The first neural network is arranged to receive a plurality of echocardiography images and to classify the images as one of at least a two-lumen tangent plane and a four-lumen tangent plane. The second neural network is arranged to receive echocardiography images comprising two or four-lumen tangent planes and to identify endocardial boundaries of a left ventricle (LV) for each tangent plane. End systolic and end diastolic images are then identified and parameters such as LV volume, ejection fraction, overall longitudinal strain, and regional longitudinal strain are calculated.

The present invention relates to a device and method for segmenting the membranous septum. The method comprises segmenting the left ventricular outflow tract, LVOT, of the heart in a 3D image data set; determining wall thickness information indicative of the wall thickness of the septum at different locations of a part of the segmented LVOT that is oriented towards the right heart chambers and right atrium; mapping the determined wall thickness information onto the surface of said part of the segmented LVOT; and segmenting the membranous septum in the 3D image data set based on the mapped wall thickness information.

The present disclosure relates to a system for assisting implantation of an implantable electrode. The system may include an implantable electrode configured to deliver pacing of the cardiac conduction system, and an external electrode configured to sense electrical activity. The system may further include a computing device operably coupled to the implantable electrode and the external electrode, and configured to monitor at least one of internal electrical activity and external electrical activity, and configured to determine cardiac conduction system capture, current impairment, and impedance based on the monitored electrical activity. The computing device may be further configured to determine, based on at least one of the cardiac conduction system capture, the current impairment, and the impedance, that the implantable electrode is proximate to the cardiac conduction system and implanted in a ventricular septum wall prior to perforation into a left ventricular cavity, and to issue a first notification.

The application provides a sepsis cardiomyopathy prognosis risk stratification system and a construction method thereof. The method first classifies SIC patients through multi-modal cardiac ultrasound, and then adopts a machine learning algorithm to screen out key ultrasound variables: left ventricular overall longitudinal strain (GLS) and tricuspid annular plane systolic excursion (TAPSE) which are strongly related to prognosis. Based on these variables, cluster analysis is performed, and SIC risk subphenotypes with significant prognosis difference are successfully constructed, and the mortality of the high-risk subtype is significantly higher. The study found that a single indicator, TAPSE, has extremely high predictive efficiency in identifying high-risk subphenotypes and can achieve rapid bedside assessment. The application first establishes a prognosis risk stratification system for SIC, simplifies the evaluation process, realizes early identification of high-risk patients, and provides key technical support for clinical precise intervention and improvement of prognosis.

The present application belongs to the technical field of heart ultrasound image processing, and provides a heart ultrasound image shooting suggestion generation method, system, medium and equipment. The technical scheme is to extract the multi-scale spatial features of the heart ultrasound image, fuse the multi-scale spatial features to obtain multi-scale spatial fusion features, perform spatial attention enhancement and channel attention enhancement on the multi-scale spatial fusion features to obtain the spatial attention enhanced and channel attention enhanced multi-scale spatial fusion features, calculate the heart contour definition and echo uniformity based on the spatial attention enhanced multi-scale spatial fusion features, calculate the heart valve definition based on the channel attention enhanced multi-scale spatial fusion features, extract the features of different time points of the heart ultrasound image, calculate the left ventricular wall motion consistency based on the features of different time points of the heart ultrasound image, perform image quality evaluation, generate shooting suggestions according to the evaluation results, and improve the extraction accuracy of important medical information.

Fully automated computer-implemented deep learning techniques of contrast-enhanced cardiac MRI segmentation are provided. The techniques may include providing cardiac MRI data to a first computer-implemented deep learning network trained in order to identify a left ventricle region of interest to generate left ventricle region-of-interest-identified cardiac MRI data. The techniques may also include providing the left ventricle region-of-interest-identified cardiac MRI data to a second computer-implemented deep learning network trained in order to identify myocardium to generate myocardium-identified cardiac MRI data. The techniques may further include providing the myocardium-identified cardiac MRI data to at least one third computer-implemented deep learning network trained to conform data to geometrical anatomical constraints in order to generate anatomical-conforming myocardium-identified cardiac MRI data. The techniques may further include outputting the anatomical-conforming myocardium-identified cardiac MRI data.

An ultrasound imaging system may receive ultrasound data of a heart, wherein the ultrasound data was acquired across at least a portion of a cardiac cycle of the heart. The ultrasound imaging system may generate a plurality of heart based measurements based, at least in part on the ultrasound data. The ultrasound imaging system may analyze, using a correlation algorithm, the plurality of heart based measurements to determine a value of cardiac pressure. In some examples, the correlation algorithm is a support vector machine (SVM) model trained on one or more of a left atrial (LA) index, a left ventricular (LV) index, LA volume parameters, echo parameters, LV strain data, LA strain data, right atrial (RA) strain data, and right ventricular (RV) strain data. In some cases, the ultrasound imaging system may weight one or more of the plurality of heart based measurements.