6 results about "Ventricular outflow tract" patented technology

The invention provides a heart implant, a delivery assembly and a heart implant system.The heart implant comprises a valve prosthesis and an anchoring nail, the anchoring nail can nail the valve prosthesis on a native tissue of an atrioventricular valve, on one hand, anchoring of the valve prosthesis in the heart is achieved, the problems of falling, shifting, twisting and the like of the valve prosthesis are avoided, and the heart implant delivery efficiency is improved; on the other hand, the compression of the valve prosthesis on the native tissue of the heart is greatly reduced so as to solve a series of problems such as ventricular structure remodeling caused by the valve prosthesis anchored through radial force, and on the other hand, the fitting gap between the valve prosthesis and the native tissue can be reduced, the sealing performance of the perivalvular is improved, the perivalvular leakage is reduced, and the heart safety is improved. On the other hand, when the valve prosthesis is nailed on the valve ring of the atrioventricular valve through the anchoring nail, the valve prosthesis is allowed to have a small axial height, and therefore the height of the valve prosthesis below the valve is reduced, and blocking of the valve prosthesis to blood entering the ventricular outflow tract is reduced.

The present invention is a high resolution, multi-function, conformal electronics device generally having a flexible and stretchable, high-density electrode array, integrated with a catheter (e.g., balloon catheter) for mapping, ablating, pacing and sensing of cardia tissue associated with heart arrhythmias. The active sensing electrode array can acquire diagnostic information including electrical (e.g., electrograms), mechanical (e.g., strain measurements), impedance (e.g., resistance), metabolic (e.g., pH or NADH measurement) from the underlying heart tissue surface with which it is in contact. The electrode array may be designed to be of various sizes and shapes to conform to the targeted cardiac tissue architecture (e.g. atrial, ventricle, right ventricular outflow tract, coronary sinus, pulmonary veins, etc.) corresponding to the specific type of cardiac arrhythmia. The present invention can precisely locate the source of arrhythmia as described above and deliver therapy from the same electrode array. This is achieved using a capacitive sensing electrode array that can not only monitor but also deliver electrical stimulation.

A left ventricle outflow tract stent and a delivery system. The left ventricle outflow tract stent comprises a stent body, the stent body consisting of a plurality of support components arranged along a first path coinciding with the path of the left ventricle outflow tract. The support components comprise inverted V-shaped frames and inverted trapezoidal frames, the inverted V-shaped frames and the inverted trapezoidal frames being alternately arranged so as to form peak regions and valley regions, and connection strips being provided on the support components. The delivery system comprises a control component, a delivery catheter, a balloon component, a guide component and a loading component, the balloon component being connected to the control component by means of the delivery catheter, the guide component being arranged at the end of the balloon component away from the delivery catheter, and the loading component being arranged at the end of the guide component away from the balloon component. By means of the control component, the delivery system is operated and controlled to accurately deliver and release the stent body on the loading component to a proper position in the left ventricle outflow tract, thereby improving the safety of minimally invasive surgeries.

The present application relates to the technical field of biology, and discloses a method for rapidly evaluating pneumonia, especially COVID-19, based on artificial intelligence, which comprises the following steps: 1) obtaining lung region ultrasound images and circulatory volume ultrasound images of a subject, wherein the circulatory volume ultrasound images comprise left ventricular outflow tract ultrasound images, left ventricular long-axis section ultrasound images and inferior vena cava long-axis section ultrasound images; 2) inputting the lung region ultrasound images and the circulatory volume ultrasound images of the subject into a trained deep convolutional neural network model, wherein the trained deep convolutional neural network model comprises a trained lung ultrasound image scoring model and a trained circulatory volume ultrasound image scoring model, so as to obtain the evaluation result of pneumonia of the subject. The present application also discloses a trained deep convolutional neural network model suitable for pneumonia, especially COVID-19, a construction method of the trained deep convolutional neural network model, and a remote operation ultrasound robot comprising the trained deep convolutional neural network model.

The present invention relates to a device and method for segmenting a membrane septum. The method comprises: segmenting the left ventricular outflow tract (LVOT) of the heart in a 3D image dataset; determining wall thickness information indicating the wall thickness of the membrane septum at different locations in the segmented left ventricular outflow tract toward the right ventricle and right atrium; mapping the determined wall thickness information to the surface of the aforementioned portion of the segmented left ventricular outflow tract; and segmenting the membrane septum in the 3D image dataset based on the mapped wall thickness information.

Systems and methods are provided for performing hemodynamic assessment of aortic valve stenosis by a cardiac computed tomography (CT) scan. The method may include performing a cardiac CT scan of a patient and determining an aortic valve area () of the patient and a left ventricular outflow tract area () of the patient via the CT scan using a computing device, calculating an anatomical dimensionless index () based on the aortic valve area () and the left ventricular outflow tract area (), and determining a severity level of aortic valve stenosis (AS) of the patient based on the aortic valve area (), the left ventricular outflow tract area (), and the anatomical dimensionless index ().