9419results about "Angiography" patented technology

A modular surgical system for use in a surgical procedure is disclosed. The modular surgical system includes a control module including a power supply and surgical modules including a first surgical module and a second surgical module. The first surgical module is arranged in a stack configuration with the control module and the second surgical module. The power supply is configured to output power to the first surgical module and the second surgical module. A power backplane is configured to deliver the power to the first surgical module and the second surgical module through the first surgical module. A control circuit is configured to adaptively adjust power allocations to the first surgical module and the second surgical module.

An electrocardiogram device configured to transmit at least one signal responsive to a wearer's cardiac electrical activity can include a disposable portion and a reusable portion configured to mechanically and electrically mate with each other. The disposable portion can include a base having at least one mechanical connector portion, a plurality of cables and corresponding external ECG electrodes, and a first plurality of electrical connectors associated with the plurality of cables. The reusable portion can include a cover having at least one mechanical connector portion, a second plurality of electrical connectors configured to electrically connect with the first plurality of electrical connectors of the disposable portion, and an output connector port configured to transmit at least one signal responsive to one or more signals outputted by the external ECG electrodes of the disposable portion.

A blood pressure monitoring system, comprising: an exciter configured to produce an acoustic signal, the exciter being provided on a first substrate portion; a detector spaced apart from the exciter, the detector being configured to detect the acoustic signal and to produce an electrical output signal, the detector being provided on a second substrate portion that is mechanically or acoustically decoupled from the first substrate portion, wherein the detector comprises at least four detectors; and a processor configured to determine a blood pressure measurement from the electrical output signal.

Apparatus and methods are described including placing, into a subject's body, a blood-pump tube, with an impeller disposed within the blood-pump tube. At least one blood-pressure-measurement element extends to at least an outer surface of the blood-pump tube, such that the distal end of the blood-pressure-measurement element is in direct fluid communication with a bloodstream of the subject outside the blood-pump tube at a location that is proximal to blood-inlet opening(s) defined by the blood-pump tube. Blood is pumped through the blood-pump tube, using the impeller. Pressure of the bloodstream of the subject outside the blood-pump tube is measured by measuring blood pressure at the distal end of the left-ventricular blood-pressure-measurement element. Other applications are also described.

A cognitive computing-based system and a method for non-invasive analysis of one or more physiological indicators using assisted transdermal optical imaging are disclosed. The cognitive computing-based system is configured to obtain multi-modal data from a plurality of sources to preprocess the multi-modal data that includes colored image data, thermal image data, physiological data, and user-provided contextual data. The preprocessed data is used for generating one or more multi-modal features. The cognitive computing-based system is configured to generate unified features representation data for machine learning (ML) models analysis using the extracted one or more multi-modal features. Further, the cognitive computing-based system is configured to perform non-invasive analysis on the unified features representation data to detect temporal trends in the unified features representation data, and generate numerical predictive insights, visual representations data, categorical predictive insights, and recommendation data of the physiological indicators based on the non-invasive analysis of the physiological indicators.

A sensor system for monitoring patients is provided. The sensor system includes a wireless charging dock, one or more patient sensors, and a processing module. The patient sensor is configured to collect patient physiological data and send the data to the processing module. The processing module wirelessly transmits the patient physiological data to a patient monitor system. The wireless charging dock is wirelessly and removably coupled to the processing module to wirelessly provide power for the processing module. The wireless charging dock is magnetically coupled to the processing module.

A noninvasive blood pressure monitor. The noninvasive blood pressure monitor may include an inflatable cuff, a pressure transducer, one or more air pumps, and a processor. The processor may control the air pump(s) so as to initiate inflation of the cuff. The processor may also identify an oscillometric signal in an output of the pressure transducer, and may determine an envelope of the oscillometric signal. The processor may also determine one or more characteristics of the envelope of the oscillometric signal, and may control the air pump(s) so as to stop inflation of the cuff based on the one or more characteristics of the envelope of the oscillometric signal.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

A computer implemented method for governing risk actions by an artificial intelligence (AI) browser, by classifying a proposed action by the AI browser based on a large language model (LLM) as safe or risky based on AI weights or based on policy rules; initiating a step up authentication flow for a risk action; presenting an action summary and required capabilities to the user for approval; and enforcing user configured spend or scope limits on the risk action.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

Various systems, devices, and methods are disclosed herein for treating acute myocardial infarction (AMI) patients using a heart pump controlled in a manner that maximizes mechanical unloading of the left ventricle in the presence of cardiovascular instability and minimizes myocardial oxygen consumption (MVO2) and consequentially infarct size to prevent the development of subsequent heart failure. In a closed feedback system, the system can include a sensor configured to generate an output used to measure or calculate a left ventricular systolic pressure (LSVP) within the left ventricle of a heart and a controller coupled to a heart pump. The controller can be configured to measure or calculate the LVSP based on the output of the sensor and to control an operation of the heart pump to maximize mechanical unloading of the left ventricle based on the measured or calculated LVSP.

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

The systems, devices, and methods presented herein use a blood pump to obtain measurements of cardiac function. The system can quantify the functioning of the native heart by measuring certain parameters / signals such as aortic pressure or motor current, then calculate and display one or more cardiac parameters and heart function parameters, such as left ventricular pressure, left ventricular end diastolic pressure, or cardiac power output. These parameters provide valuable information to a user regarding current cardiac function, as well as positioning and function of the blood pump. In some embodiments, the system can act as a diagnostic and therapeutic tool. Providing cardiac parameters in real-time, along with warnings about adverse effects and recommendations to support cardiac function, such as increasing or decreasing the volumetric flow rate of blood pumped by the device, administering pharmaceutical therapies, and / or repositioning the blood pump allow clinicians to better support and treat cardiovascular disease.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

An apparatus for treating or preventing a valvular disease, said apparatus comprising : - a ultrasound probe (2) located externally to a heart (H) of patient ( P), able to produce ultrasound waves focused inside said heart and suitable to generate, at a focal spot, a pressure sufficient to result in cavitation, - an imaging device (4) for mapping in real time a treatment region (C) of a cardiac valve of the patient, said treatment region comprising at least one leaflet of the cardiac valve, - a controller (5, 7, 8 ) configured for driving the ultrasound probe to emit a sequence of focused ultrasound waves, the controller being further configured for steering the focused ultrasound waves so as to scan the entire treatment region to soften the tissues of the treatment region. A method for treating or preventing valvular disease, carried out using said apparatus.

Systems, devices, and methods for electrically stimulating peripheral nerve(s) to treat various disorders are disclosed, as well as signal processing systems and methods for enhancing diagnostic and therapeutic protocols relating to the same. Personalized therapy based on algorithms and aggregated data are also provided.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably improved.

Systems and methods are disclosed for determining blood flow characteristics of a patient. One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient-specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.

The invention relates to the technical field of health status assessment, in particular to a cardiovascular disease layered early warning system, which is characterized in that blood vessel compliance and pulse wave velocity are extracted, arterial dilation capacity is measured, blood vessel elasticity is calculated, blood perfusion distribution is detected, and abnormal vasoconstriction is classified on the basis of a hemodynamic state and a heart electrical activity state; in combination with the thickness of the blood vessel wall and the blood vessel stenosis rate, the plaque deposition condition is analyzed, the arteriosclerosis score is calculated, the thrombosis tendency is detected, the local blood flow blocking degree is quantified, the isolation forest algorithm is adopted, the blood flow dynamic change is calculated, the heart rhythm abnormality is analyzed, the acute vascular occlusion probability is measured, and the sudden heart rhythm abnormality risk is evaluated. And based on a gradient boosting tree algorithm, screening high-risk cases, matching medical intervention measures, setting a risk buffer mechanism, adjusting an individual health management strategy, and optimizing a cardiovascular risk hierarchical early warning system.

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

A computer-implemented method for processing electronic images for subject-specific two-dimensional modeling of a subject's vasculature, comprising: receiving one or more subject-specific three-dimensional models of the subject's vasculature, wherein the subject-specific three-dimensional model includes a representation of the vascular tree structure; determining a two-dimensional viewing plane; determining a projection of the representation of the vascular tree structure of the one or more subject-specific three-dimensional model onto the two-dimensional viewing plane; generating one or more subject-specific three-dimensional models around the representation of the vascular tree structure; and generating a two-dimensional image depicting the one or more generated models.

A computer-implemented method for subject-specific two-dimensional modeling of a subject's vasculature may comprise: receiving a subject-specific three-dimensional model of the subject's vasculature, wherein the subject-specific three-dimensional model includes one or more centerlines; determining a two-dimensional viewing plane; determining a projection of the one or more centerlines of the subject-specific three-dimensional model onto the two-dimensional viewing plane; generating one or more models around the one or more centerlines; and generating a two-dimensional image depicting the one or more models.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient's heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.