6 results about "Left cardiac chamber" patented technology

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.

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 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.

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.

This invention relates to the field of intensive care technology, specifically to an integrated system for real-time hemodynamic monitoring and early warning of critically ill ICU patients. The system includes: a physiological signal synchronous acquisition and quality enhancement module for acquiring and processing multi-channel physiological waveforms; a core physiological state vector calculation module, based on a first-principles model of the circulatory system, for real-time calculation of the left ventricular maximum contractile elasticity Emax, representing myocardial contractility, and the gain coefficient γk, representing preload responsiveness; a dynamic risk assessment and early warning module, which predicts decompensation risk and triggers early warnings by fusing temporal features through a bidirectional gated circulatory unit neural network; a personalized physiological simulator module, which dynamically calibrates a cardiovascular mechanism model using an unscented Kalman filter to generate a physiological image of the patient; and a system adaptive evolution module, which iteratively optimizes model parameters through a two-layer closed-loop mechanism. This invention achieves closed-loop management from accurate perception and risk prediction to personalized decision support.

Described herein are systems and methods for synchronizing electrical activity monitored by a plurality of external electrodes with supplemental cardiac data for assessing a patient's cardiac condition and configuring cardiac therapy. The supplemental cardiac data can include one or more markers indicative of a cardiac event occurrence and / or representative of at least one of a cardiac electrical signal, a heart sound, a cardiac pressure, a blood flow, and an estimated instantaneous flow waveform provided by a left ventricular assist device.