130 results about "Ultrasonic cardiography" patented technology

The invention includes a novel method and system to achieve leaflet coaptation in a cardiac valve percutaneously by creation of neochordae to prolapsing valve segments. This technique is especially useful in cases of ruptured chordae, but may be utilized in any segment of prolapsing leaflet. The technique described herein has the additional advantage of being adjustable in the beating heart. This allows tailoring of leaflet coaptation height under various loading conditions using image-guidance, such as echocardiography. This offers an additional distinct advantage over conventional open-surgery placement of artificial chordae. In traditional open surgical valve repair, chord length must be estimated in the arrested heart and may or may not be correct once the patient is weaned from cardiopulmonary bypass. The technique described below also allows for placement of multiple artificial chordae, as dictated by the patient's pathophysiology.

A method for relating to each other cardiovascular and sleep disordered breathing conditions of a patient. The patient's heart rate and / or detailed echocardiogram data is monitored and recorded continuously or periodically together with sleep disordered breathing information on similar time scales. Changes in the patient's heart rate associated with changes in sleep disordered breathing can then be observed. More specifically, positive airway pressure at therapeutic levels for treatment of sleep disordered breathing is applied while events associated with the treatment of the patient's sleep disordered breathing are detected and recorded. At the same time, information concerning the cardiovascular condition of the patient is stored and the stored information concerning the cardiovascular condition of the patient and the recorded events associated with the treatment of the patient's sleep disordered breathing are related to each other.

The present invention thus provides a simple and automatic method for determining an optimal AV interval and / or range of AV intervals for, in an exemplary embodiment, LV-only pacing. Such a method provides significant advantages for patients while reducing burdens related to post-implant follow-up by clinicians in that it greatly reduces the need for doing echocardiographic-based AV interval optimization procedures as well as providing a way to dynamically optimize AV intervals as the patient moves about their activities of daily living (ADL).

A method for tracking motion and shape changes of a deformable model in a volumetric image sequence. The method is operable to predict the surface of a space from a 3D image, such as cardiac structures from a 3D ultrasound. The shape and position of a deformable model is predicted for each frame of an ultrasound image. Edge detection is then performed for each predicted point on the deformable model perpendicular to the model surface. The distances between the predicted and measured edges for the deformable model are measurements for a Kalman filter. The measurements are coupled with noise values that specify the spatial uncertainty of the edge detection. The measurement data are subsequently summed together in information space and combined with the prediction in the Kalman filter to estimate the position and deformation for the deformable model. The deformable model is then updated to generate an updated surface model.

A method of and system for transmitting video images preferably allows a specially trained individual to remotely supervise, instruct, and observe administration of medical tests conducted at remote locations. This system preferably includes a source device, a transmitting device, and at least one remote receiving device. The transmitting device, and the remote receiving device communicate over a network such as any appropriate data network. The transmitting device transmits the video images to the remote receiving device either for live display from the source device or for pre-recorded display from a video recorder device. The remote receiving device is also capable of communicating with the transmitting device while simultaneously receiving video images to provide remote control. The source device is preferably a medical test device such as an ultrasound, a sonogram, an echocardiogram, an angioplastigram, and the like. The transmitting device captures the video images in real-time from the source device and compresses these video images utilizing a compression method prior to transmitting data representing the video images to the remote receiving device. The compressor and compression method preferably utilize data structures comprising line number data structures and the repeat data structures. Remote users utilizing the remote receiving devices are capable of viewing a live stream of video and remotely controlling a number of parameters relating to the source device and the transmitting device. Such parameters include compression method, image quality, storage of the video images on the transmitting device, manipulating and controlling the source device, and the like.

An echocardiogram report generation method and system are disclosed for creating a complete medical report describing an echocardiogram without dictation, transcription or typing. The report is tailored to describe an individual patient and generated in complete grammatically complex sentences in response to the user's input with a text report that is complete and understandable for a primary care physician. The method and system provides the user the parallel options of an anatomical structure approach and a disease-specific approach in order to amplify the description of the echocardiogram in specific areas and give a more meaningful report. The echo report generated by the method and system creates a table of measurements and calculations in compact form, omitting any data fields not used. The method and system guides the user through the various areas of the heart which merit comment on the report and provides clear color coded segments of the heart muscle and provides several heart segment classifications so that the user can use his favored classification scheme. Diagnostic possibilities are provided for inclusion in the report conclusions.

Determining an optimal atrioventricular interval is of interest for proper delivery of cardiac resynchronization therapy. Although device optimization is gradually and more frequently being performed through a referral process with which the patient undergoes an echocardiographic optimization, the decision of whether to optimize or not is still generally reserved for the implanting physician. Recent abstracts have suggested a formulaic approach for setting A-V interval based on intrinsic electrical sensing, that may possess considerable appeal to clinicians versus a patient average nominal A-V setting of 100 ms. The present invention presents a methods of setting nominal device settings based on entering patient cardiac demographics to determine what A-V setting may be appropriate. The data is based on retrospective analysis of the MIRACLE trial to determine what major factors determined baseline A-V settings.

The invention relates methods of reducing the risk or preventing the occurrence of an adverse event (AE) or a serious adverse event (SAE) associated with a medical treatment comprising inhalation of nitric oxide.

In a method of detecting microemboli in a patient, an echography probe is applied to a selected location of the patient. An ultrasonic signal is sensed with the echocardiography probe. The ultrasonic signal is processed with a processor to determine a systemic embolic load in the selected location. A representation of a systemic embolic load indicated by the ultrasonic signal is presented to a user.

Transesophageal echocardiography is implemented using a miniature transversely oriented transducer that is preferably small enough to fit in a 7.5 mm diameter probe, and most preferably small enough to fit in a 5 mm diameter probe. Signal processing techniques improve the depth of penetration to the point where the complete trans-gastric short axis view of the left ventricle can be obtained, despite the fact that the transducer is so small. The reduced diameter of the probe (as compared to prior art probes) reduces risks to patients, reduces or eliminates the need for anesthesia, and permits long term direct-visualization monitoring of patients' cardiac function.

Acute and chronic heart disease is treated using a rational, multi-tier approach. A patient is pretreated with growth factor proteins or gene therapy, followed by the administration of adult stem cells. The progress of treatment is continuously monitored by echo-cardiogram with growth factor treatment and / or stem cell administration adjusted according to the results of the echo-cardiogram or clinical status of the patient. Heart disease is also treated by a method that comprises administration of a therapeutically effective amount of a growth factor protein by oral inhalation therapy.

In heart failure (HF) patients diagnosed according to the New York Heart Association (NYHA) rating scale as either Class III or Class IV, with QRS duration ≧120 ms, left ventricular (LV) EF≦35% (when in sinus rhythm), cardiac resynchronization therapy (CRT) can predict sustained improvement in at least LV structure and function. The knowledge of aetiology of a subject's HF status and of a few simple echocardiographic characteristics provides useful information as to whether such patients and undergo significant and beneficial reverse remodelling of the LV in particular and, in the long term can be expected to experience an improvement in all-cause mortality, for example such patients can be reasonably expected to survive and enjoy a relatively enhanced quality of life (QOL). A patient who qualifies according to the invention can be termed a reverse remodelling responder (RRR) or the like.

The invention relates to a method and device for calculating ultrasonic cardiogram cardiac parameters and measuring myocardial strain. A trained neural network is employed for executing the processingof the method, and the processing at least comprises: the classification of a cardiac ultrasonic video to obtain a section classification result; image segmentation of the section classification result to obtain a segmentation result; and acquisition of cardiac parameters and myocardial strain according to the segmentation result. In embodiments of the invention, automatic section classificationprocessing and image segmentation are carried out on the cardiac ultrasonic video through the trained neural network, and the measurement results of the cardiac parameters and the myocardial strain are further automatically obtained, so the workload of doctors is effectively reduced, and working efficiency is improved.

A support device for holding a transesophageal echocardiography probe is disclosed, the device comprising a liner component forming an interior portion and an exterior portion, the interior portion of the liner configured to receive the transesophageal echocardiography probe therein; a base component forming a support region having a support surface, the support region of the base component configured to receive the liner component therein; and an attachment component in connection with the base component, the attachment component configured to attach the base component to a support structure. A method for holding a transesophageal echocardiography probe is disclosed, the method comprising attaching an anchor portion to a support structure so as to attach a base portion to the support structure; and securing a handle of the transesophageal echocardiography probe and a liner in the base portion by positioning a cord of the transesophageal echocardiography probe through the slot of the base portion and positioning the handle into a top end of the base portion with a liner substantially surrounding the handle.

A computer vision pipeline is provided for fully automated interpretation of cardiac function, using a combination of machine learning strategies to enable building a scalable analysis pipeline for echocardiogram interpretation. Videos from patients with heart failure can be analyzed and processed as follows: 1) preprocessing of echo studies; 2) convolutional neural networks (CNN) processing for view identification; 3) segmentation of chambers and delineation of cardiac boundaries using CNNs; 4); particle tracking to compute longitudinal strain; and 5) targeted disease detection.

The invention relates to an echocardiographic ventricle segmentation method and device based on deep learning and a deformation model, and aims at ironing out a defect that a conventional manual boundary marking method exerts a big negative impact on the calculation of the related indexes of a ventricle because the conventional manual boundary marking method usually consumes a large amount of manpower and material resources and the marking results of different persons are different. The method comprises the steps: carrying out the training of a ventricle coarse segmentation model through manually marked training data, and obtaining a coarse segmentation training result; calculating the central point of the coarse segmentation training result on each section, carrying out the fitting of oneline through all central points, and calculating the mean value of the distances between the central points in a direction perpendicular to the line and an outer edge of the coarse segmentation training result as the radius; carrying out the resampling according to the calculated central points and the radius, and reconstructing a three-dimensional initialization model based on a sampling result;and carrying out the fine segmentation of the ventricle coarse segmentation result through the deformation model. The method is suitable for the ventricle image processing.

The invention relates methods of reducing the risk or preventing the occurrence of an adverse event (AE) or a serious adverse event (SAE) associated with a medical treatment comprising inhalation of nitric oxide.

The invention discloses a method for establishing image data through nuclear magnetic resonance and belongs to the technical field of medical images. The method comprises the following steps that nuclear magnetic resonance image sequential data of a target zone in the first pass process of a contrast agent are extracted; registration is conducted on an image sequence, so that displacement deviation of the target zone in the image sequence is eliminated; the myocardium outline in the target zone of an image is segmented, so that an inner outline and an outer outline of the myocardium are obtained; quantitative analysis is conducted, the myocardium in the image is segmented into a plurality of partial zones according to an ultrasonic cardiogram seventeen-segmentation division method along the outline, a pixel mean value of each zone is calculated, and a data curve of the relation of the signal intensity and time is obtained. The nuclear magnetic resonance image data are collected, image registration is conducted, the myocardium outline image data are obtained through segmentation, quantitative analysis is conducted, a data model and an accurate data analysis platform are established, and therefore waste of resources is avoided.

The invention provides a system and device for improving the segmentation accuracy of left ventricles of multiple heart views based on deep learning, and the system comprises a data collection modulewhich is configured to collect the image data of ultrasonic cardiograms of a plurality of different views to form an original image data set, and collecting a to-be-processed echocardiogram as to-be-segmented data; a preprocessing module which is configured to preprocess the original image data set to form an experimental data set; a training module which is configured to construct a deep neural network training model, input the experimental data set into the training model for training, and when a loss function value in the training model is not reduced any more, stop the training of the training model and save model parameters; a data processing module which is configured to input a to-be-processed echocardiogram into the training module for storing the model parameters to obtain an endocardial and epicardial segmentation result. According to the invention, the training precision of processing different ventricular images is improved, and then the heart view segmentation precision isimproved.

The present invention relates to compounds of Formula (Ia) and pharmaceutical compositions thereof that modulate the activity of the beta-3 adrenergic receptor. Compounds of the present invention and pharmaceutical compositions thereof are directed to methods useful in the treatment of a beta-3 adrenergic receptor-mediated disorder, such as, heart failure; cardiac performance in heart failure; mortality, reinfarction, and / or hospitalization in connection with heart failure; acute heart failure; acute decompensated heart failure; congestive heart failure; severe congestive heart failure; organ damage associated with heart failure (e.g., kidney damage or failure, heart valve problems, heart rhythm problems, and / or liver damage); heart failure due to left ventricular dysfunction; heart failure with normal ejection fraction; cardiovascular mortality following myocardial infarction; cardiovascular mortality in patients with left ventricular failure or left ventricular dysfunction; left ventricular failure; left ventricular dysfunction; class II heart failure using the New York Heart Association (NYHA) classification system; class III heart failure using the New York Heart Association (NYHA) classification system; class IV heart failure using the New York Heart Association (NYHA) classification system; LVEF<40% by radionuclide ventriculography; LVEF≤35% by echocardiography or ventricular contrast angiography; and conditions related thereto.

The invention discloses a mitral valve opening area detection method, system and device based on artificial intelligence. The method comprises the steps that a two-dimensional section video reflectinga mitral valve structure in an ultrasonic cardiogram of a detection object is acquired; inputting the two-dimensional section video into a trained deep learning classification model, and obtaining aprediction value of the two-dimensional section video containing mitral valve stenosis image features; if the prediction value meets a preset condition, inputting the heart tip four-cavity heart mitral valve continuous Doppler frequency spectrum section image of the detection object into an image segmentation network model, and obtaining a frequency spectrum oscillogram corresponding to the section image; and predicting the mitral valve opening area according to the frequency spectrum oscillogram. According to the method, the system and the equipment, the accuracy and the consistency of ultrasonic detection are greatly improved.

There is provided a computer-implemented method for analysing echocardiograms, the method comprising: obtaining (302) a plurality of pairs of consecutive echocardiograms for a plurality of subjects from a database (200), each echocardiogram having an associated indication of the content of the echocardiogram; analysing (304) each pair of consecutive echocardiograms to determine an associated class, the class indicating whether there is a change or no change between the consecutive echocardiograms in the pair; for each pair of consecutive echocardiograms, determining (306) an abstract representation of each echocardiogram by performing one or more convolutions and / or reductions on the echocardiograms in the pair, the abstract representation comprising one or more features indicative of the class of the pair; and training (308) a predictive model to determine a class for a new pair of echocardiograms based on the abstract representations for the plurality of pairs of consecutive echocardiograms.

An intracardiac imaging system 10 configured to display electrode visualization elements within an intracardiac echocardiography image 12 where the electrode visualization elements represent intracardiac electrodes in close proximity to the plane of the image 12. The system 10 further allows cross sections of tissue structures embodied in intracardiac echocardiography images 12 to be modeled within a visualization, navigation, or mapping system 20 when automatically segmented to generate shell elements 36 for modifying the modeled tissue structures.

Techniques for providing expert instruction in interpreting outputs of a test instrument to a student who is at a location remote (103) from the expert. The student makes outputs from the test instrument and an interpretation of the outputs available to an expert (129) via a network (113) and the expert makes a comment available to the student in the same fashion. The outputs, the interpretation, and the comment may be made available in real time or in non-real time. In the non-real-time embodiment, the student makes his interpretation by selecting from menus of possible observations and the expert makes comments in the same fashion. Inputs from the menu selections are stored in a database record and a report is generated from the record. One application of the invention is teaching echocardiography.

The invention provides a deep learning atrial septal defect detection method and device, and the method comprises the steps: obtaining an ultrasonic cardiogram, preprocessing the ultrasonic cardiogram, and extracting a region of interest; carrying out feature extraction on the region of interest, and identifying a subsword process atrial septal frontal section, a subsword process atrial septal sagittal section, a cardiac apex four-cavity cardiac tangent plane, a low-position paracarpal four-cavity cardiac tangent plane and a paracarpal aorta short-axis tangent plane; detecting the minimum distance point of the atrial septal defect; segmenting heart structures of a subsword atrial septal frontal section, a subsword atrial septal sagittal section, a cardiac apex four-cavity cardiac tangent plane, a low paracardial four-cavity cardiac tangent plane and a paracardial aorta minor axis section to obtain segmentation results, the heart structures including a left atrium, a right atrium, a left ventricle, a right ventricle, an aorta and pulmonary arteries; and according to the segmentation results, filtering the detected minimum distance point bounding box of the atrial septal defect, andtaking the filtered result as an atrial septal defect detection result.