A system is described for generating a revascularization score for a blockage of a coronary artery in a heart. The system accesses indications of viability of myocardial tissue in the heart, a blockage state of the blockage that includes a blockage location and a blockage amount, and the perfusion territory of the myocardial tissue. Based on the myocardial tissue state, blockage state, and perfusion territory, the system generates a revascularization score for the blockage. The system generates a graphic of the heart that illustrates coronary arteries, myocardial tissue state, blockage state, and the revascularization score. The system displays the graphic to provide a visual representation of the revascularization score for the blockage of the coronary artery.
A61B 6/50 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body partsApparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific clinical applications
A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment
A system is described for generating a revascularization score for a blockage of a coronary artery in a heart. The system accesses indications of viability of myocardial tissue in the heart, a blockage state of the blockage that includes a blockage location and a blockage amount, and the perfusion territory of the myocardial tissue. Based on the myocardial tissue state, blockage state, and perfusion territory, the system generates a revascularization score for the blockage. The system generates a graphic of the heart that illustrates coronary arteries, myocardial tissue state, blockage state, and the revascularization score. The system displays the graphic to provide a visual representation of the revascularization score for the blockage of the coronary artery.
A61B 6/46 - Arrangements for interfacing with the operator or the patient
A61B 6/50 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body partsApparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific clinical applications
A method for localizing the source of an arrhythmia within a heart wall is disclosed. The method involves accessing an indication of the source location of the arrhythmia within the endocardium. A normal vector is generated that is normal to the endocardium at the source location in the direction of the epicardium layer. An activation vector indicating the direction of electrical force of the heart during an initial stage of depolarization is determined. The depth angle between the normal vector and the activation vector is determined, and the depth of the source location within the heart wall is indicated based on a depth angle.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Systems are provided for synthesizing leads of an electrocardiogram (ECG) based on a subject ECG collected from a subject and converting a nonstandard ECG based on a nonstandard placement of electrodes to a standard ECG with a standard placement of electrodes. The described systems may generate simulated ECGs based on simulations of electrical activity of hearts having different heart configurations. From each simulation, simulated ECGs are generated assuming a specification of electrode position(s) for each lead of an ECG. The systems identify a simulated ECG that is similar to the subject ECG. Based on the simulation from which that simulated ECG was generated, the systems identify a synthesized ECG or converted ECG.
A system is provided for generating an ablation plan for an ablation procedure to be performed on a body part of a patient having an abnormal pattern of electrical activity. The system receives patient data that includes a patient cardiogram, a patient body part image; and patient health data. The system employs an ablation target system to identify an ablation target within the body part based on at least some of the patient data. The system also employs an ablation plan system to identify, based on at least some of the patient data and the ablation target, an ablation plan that includes target parameter values for ablation device parameters for controlling an ablation device. The ablation plan system is developed based on data that includes data sets with patient data associated with an ablation plan. The system then outputs an indication of the ablation target and the ablation plan.
A61B 34/10 - Computer-aided planning, simulation or modelling of surgical operations
A61B 18/02 - Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by cooling, e.g. cryogenic techniques
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
A61B 18/00 - Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
A system is provided for generating an ablation plan for an ablation procedure to be performed on a body part of a patient having an abnormal pattern of electrical activity. The system receives patient data that includes a patient cardiogram, a patient body part image; and patient health data. The system employs an ablation target system to identify an ablation target within the body part based on at least some of the patient data. The system also employs an ablation plan system to identify, based on at least some of the patient data and the ablation target, an ablation plan that includes target parameter values for ablation device parameters for controlling an ablation device. The ablation plan system is developed based on data that includes data sets with patient data associated with an ablation plan. The system then outputs an indication of the ablation target and the ablation plan.
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.
A system is provided that employs a machine learning (ML) decision tree to provide a treatment analysis for an arrhythmia. The ML decision tree includes decision nodes (non-leaf nodes) and treatment analysis nodes (leaf nodes). Each decision node corresponds to a feature derived from electronic health records and has branches corresponding to feature values. A treatment analysis node corresponds to a treatment analysis based on feature values of a path from the root node to that treatment analysis node. To provide a treatment analysis for a candidate, the system identifies a path from the root node to a treatment analysis node based on a candidate feature vector derived from an electronic health record of the candidate and outputs the treatment analysis of the treatment analysis node of the identified path.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
A61B 34/10 - Computer-aided planning, simulation or modelling of surgical operations
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A system is provided that employs a machine learning (ML) decision tree to provide a treatment analysis for an arrhythmia. The ML decision tree includes decision nodes (non-leaf nodes) and treatment analysis nodes (leaf nodes). Each decision node corresponds to a feature derived from electronic health records and has branches corresponding to feature values. A treatment analysis node corresponds to a treatment analysis based on feature values of a path from the root node to that treatment analysis node. To provide a treatment analysis for a candidate, the system identifies a path from the root node to a treatment analysis node based on a candidate feature vector derived from an electronic health record of the candidate and outputs the treatment analysis of the treatment analysis node of the identified path.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Systems and methods are provided for generating a stroke risk assessment machine learning (ML) model. A system generates an initial stroke risk ML model based on training data that includes, for each of a plurality of patients, features derived from a cardiogram of the patient and an indication of whether the patient had a stroke. The system runs simulations of electrical activity of a heart based on heart models having various morphological and physiological characteristics. The system generates simulated cardiograms based on the simulated electrical activity the simulations. The system applies the initial stroke risk ML model to features derived from a simulated cardiogram to generate a simulated stroke risk assessment. The system then generates a final stroke risk ML model based on features derived from the simulated cardiograms and the simulated stroke risk assessments.
A system for generating a machine learning (ML) model to identify an atrial flutter (AFL) type of a cardiac arrhythmia is provided. For each of a plurality of cardiograms, the system generates training data by identifying one or more portions of that cardiogram that relate to the AFL type to which that cardiogram is mapped. For each of a plurality of the portions, the system generates a feature vector that includes the portion and the additional features and a label that is based on the AFL type. The system trains the ML model using the training data to learn weights for the ML model. The ML model inputs a cardiogram and additional features and outputs an AFL type.
A system for identifying a patient device location of a patient stimulation device within the heart of a patient. The system receives a patient electrocardiogram that is collected while the patient stimulation device is activated and while cardiac tissue is not captured. The electrocardiogram reflects current through blood within the heart resulting from the activation. The system determines a device location based on an association between library electrocardiograms and library device locations and based on similarity between the library electrocardiograms and the patient electrocardiogram. A library electrocardiogram represents an electrocardiogram that would be collected when a stimulation device is activated within the heart of a patient at the associated library device location. The system outputs an indication of the determined device location to indicate the patient device location of the patient stimulation device when the patient electrocardiogram was collected.
Methods and computer systems are described that classify a cardiogram as being an atrial fibrillation (AF) or ventricular fibrillation (VF) cardiogram, automatically detect an AF epoch within an AF cardiogram, and automatically detect a VF epoch within a VF cardiogram. A classification and identification (C&I) system includes a classification system, an AF identification system, and a VF identification system. The C&I system processes cardiograms collected from patients to classify the cardiograms as being AF cardiograms or VF cardiograms and to identify AF epochs within the AF cardiograms or VF epochs within the VF cardiograms. The C&I system may then identify an AF source location of an AF based on the AF epochs and a VF source location of a VF based on the VF epochs. The C&I system may display a graphic of a heart that includes an indication of a source location.
A61B 5/367 - Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Systems are provided for synthesizing leads of an electrocardiogram (ECG) based on a subject ECG collected from a subject and converting a nonstandard ECG based on a nonstandard placement of electrodes to a standard ECG with a standard placement of electrodes. The described systems may generate simulated ECGs based on simulations of electrical activity of hearts having different heart configurations. From each simulation, simulated ECGs are generated assuming a specification of electrode position(s) for each lead of an ECG. The systems identify a simulated ECG that is similar to the subject ECG. Based on the simulation from which that simulated ECG was generated, the systems identify a synthesized ECG or converted ECG.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.
Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.
patents
