9 results about "Ventricular volume" patented technology

The invention provides a system and a method for drawing a ventricular contour and calculating ejection fraction by medical color Doppler ultrasound equipment. The system comprises an image acquisition and caching module for acquiring a plurality of frames of B-mode ventricular images; a background noise and ventricle distinguishing module obtains background noise and ventricle structure images; the contour boundary determination module obtains ventricle contour boundary points; the contour coordinate category distinguishing module performs category distinguishing on the ventricular contour boundary points and determines the position of the valve according to a category distinguishing result; a valve boundary point determination module determines a valve boundary point; the calculation module is used for calculating ventricular volume and ejection fraction; the display module is used for displaying the target ventricular image, the ventricular volume and the ejection fraction; according to the method, the requirement for noise can be effectively met, the ventricular contour can be accurately determined, the accuracy and efficiency of judging the cardio-pulmonary function can be guaranteed, and meanwhile the real-time state can be known more visually and efficiently through visual image superposition and numerical value display.

The invention provides a ventricular elasticity value estimation method and device.The method comprises the steps that a first parameter and a second parameter are obtained, the first parameter comprises at least one of target ventricular pressure, target aortic pressure and target pulmonary arterial pressure of a target user, and the second parameter comprises at least one of the target ventricular pressure, the target aortic pressure and the target pulmonary arterial pressure; the second parameter comprises the pumping flow of the target ventricular assist device and the cardiac cycle of the target user; inputting the first parameter and the second parameter into a target decision model, and outputting a target ventricular volume; and estimating a target ventricular elasticity value according to the ratio of the target ventricular pressure to the target ventricular volume. The ventricular volume of the current user is estimated through the decision model, the estimated ventricular elasticity value is calculated according to the ratio of the ventricular pressure to the ventricular volume, and the ventricular elasticity value of the patient can be accurately predicted in real time without depending on ultrasonic equipment.

Systems, devices, user interfaces, and methods for managing patient conditions. A system for monitoring and / or managing a patient suffering from a brain condition may include a controller obtaining and / or storing data related to one or more monitored patient parameters contributing to the brain condition of the patient and displaying values of the one or more monitored patient parameters in trend lines over time on a user interface. The displayed trend lines may represent values of ventricular CSF volume, values of IVH volume, total ventricular volume in the brain, and / or values of other suitable parameters. The data related to the one or more monitored patient parameters may include images of the patient's brain and / or data derived from images of the patient's brain and / or suitable patient data.

The invention relates to a cardiac assist system comprising: - an intraventricularly implantable cardiac pump, - a processing unit, - at least one lead intended to be placed on an external wall of the heart. The processing unit includes a cardiac pump management function, a pacing function, and / or a defibrillation function. The processing unit is configured to: - perform cardiographic impedance measurements between said at least one lead and a metallic part of the cardiac pump in order to determine the following hemodynamic parameters: - a cardiac electromechanical delay from the cardiographic impedance measurements, - a blood filling time in the heart from the cardiographic impedance measurements, - a blood ejection time from the heart from the cardiographic impedance measurements, and - a change in right ventricular and / or left ventricular volume over time.Figure for the abridged version: Fig. 1.

Methods and systems are disclosed for creating and using a neural network model to estimate a cardiac parameter of a patient, and using the estimated parameter in providing blood pump support to improve patient cardiac performance and heart health. Particular adaptations include adjusting blood pump parameters and determining whether and how to increase or decrease support, or wean the patient from the blood pump altogether. The model is created based on neural network processing of data from a first patient set and includes measured hemodynamic and pump parameters compared to a cardiac parameter measured in situ, for example the left ventricular volume measured by millar (in animals) or inca (in human) catheter. After development of a model based on the first set of patients, the model is applied to a patient in a second set to estimate the cardiac parameter without use of an additional catheter or direct measurement.

The invention discloses an artificial intelligence platform for comprehensive radiological image analysis across CT, MRI, and X-ray modalities. The system automatically detects and quantifies cardiac, pulmonary, and oncological abnormalities through multiple AI modules including heart localization, cardiac MRI quantification, pneumonia classification, and tumor segmentation / grading. The platform provides automated tumor detection, 3D volumetric segmentation, and malignancy grading and treatment planning. The system integrates hybrid CNN-Transformer networks, attention-based 3D segmentation, and multi-modal feature fusion, offering precise quantitative metrics for tumor size, volume, shape, and malignancy probability. It also a cardiac MRI analysis module measures atrial and ventricular volumes and detects atrial fibrillation or mitral valve stenosis. It calculates the cardiothoracic ratio and detects heart displacement. DICOM / PACS integration and federated learning enable clinical deployment and cross-institutional adaptability while preserving patient data privacy and assists clinicians in accurate and rapid diagnosis while minimizing human error. This first-in-world platform facilitates early detection, staging, grading, and treatment planning with high reproducibility and explainable outputs.

The application discloses a heart ultrasound image optimization enhancement method based on image filtering and belongs to the technical field of heart ultrasound images. The heart ultrasound image optimization enhancement method based on image filtering comprises the following steps: S1, obtaining a heart ultrasound binary image; S2, obtaining a heart ultrasound image to be processed; S3, highlighting the edge features of a blood vessel wall and myocardial tissue; S4, obtaining an enhanced heart ultrasound image; and S5, obtaining an optimized and enhanced heart ultrasound image. The application solves the problem that the prior art is prone to affecting the overall consistency judgment of heart cavities, valves and other structures, can more intuitively display a heart disease area, reduces the misjudgment of medical staff caused by noise or low contrast, can effectively capture texture information at different levels, enhances the recognition ability of complex heart structures, and further more accurately quantifies myocardial strain rate, ventricular volume and other parameters, thereby providing a reliable basis for evaluating heart function.

The invention discloses a full-automatic cardiac ultrasonic ejection fraction analysis method, system and device and a medium, relates to the technical field of image processing and deep learning, and solves the technical problem that EDV and ESV cannot be accurately predicted due to the fact that a dynamic heart cannot be accurately analyzed. According to the technical scheme, the method is characterized in that noise in a cardiac ultrasound image is segmented and resisted through an image segmentation model, the actual ventricular volume is calculated, whether the blood supply function of a patient is impaired or not is judged, the boundary of ventricular wall movement is obtained in an image registration model movement tracking mode, and therefore the segmentation model is assisted in determining the correct boundary; the tiny movement of valve closure can be captured through movement tracking to help judge whether the tiny movement is a special frame in a movement period or not; and finally, introducing regression prediction of ejection fraction EF, and naturally constraining the mask predicted in the motion period from the ventricular volume ratio, so that the densely predicted mask is smoother and more accurately attached to the chamber area.

This invention discloses a method, electronic device, and storage medium for detecting imaging features of hydrocephalus based on neural networks. The method includes collecting and labeling brain imaging data of patients with normal pressure hydrocephalus, and performing preprocessing; using a neural network to segment the ventricular region in thick-slice brain imaging data to generate a binary image sequence; training a neural network model for interpolation using thin-slice brain imaging data, continuously adjusting parameters until optimal; inputting the binary segmentation result of the thick-slice image along with the original thick-slice image into the trained interpolation network, outputting an interpolated binary segmented image; counting the number of voxels in the ventricular portion of the interpolated binary segmented image, and calculating the actual volume of the ventricular cavity based on the image scanning interval information. This invention reduces the error in calculating ventricular volume by segmenting images when the scanning interval is too large, obtaining accurate ventricular volume data, thereby optimizing the computer-aided diagnosis process for hydrocephalus.