Abstract: A computer-implemented method for personalized assessment of patients with acute coronary syndrome (ACS) includes extracting (i) patient-specific coronary geometry data from one or more medical images of a patient; (ii) a plurality of features of a patient-specific coronary arterial tree based on the patient-specific coronary geometry data; and (iii) a plurality of ACS-related features from additional patient measurement data. A surrogate model is used to predict patient-specific hemodynamic measures of interest related to ACS based on the plurality of features of the patient-specific coronary arterial tree and the plurality of ACS-related features from the additional patient measurement data.

Abstract: Machine learning is used to assess data for a patient in an emergency, providing rapid diagnosis based on a large amount of information. Assistance in triage may be provided. Given the large variety of patients and conditions that may occur, the machine learning may rely on synthetically generated images for more accurate prediction. The machine learning may accurately predict even with missing information and may be used to determine what missing information for a given patient is more or less important to obtain.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: A computed tomography (CT)-based clinical decision support system provides fractional flow reserve (FFR) decision support. The available data, such as the coronary CT data, is used to determine whether to dedicate resources to CT-FFR for a specific patient. A machine-learnt predictor or other model, with access to determinative patient information, is used to assist in a clinical decision regarding CT-FFR. This determination may be made prior to review by a radiologist and/or treating physician to assist decision making.

Abstract: Systems and methods for model augmentation include receiving intra-operative imaging data of an anatomical object of interest at a deformed state. The intra-operative imaging data is stitched into an intra-operative model of the anatomical object of interest at the deformed state. The intra-operative model of the anatomical object of interest at the deformed state is registered with a pre-operative model of the anatomical object of interest at an initial state by deforming the pre-operative model of the anatomical object of interest at the initial state based on a biomechanical model. Texture information from the intra-operative model of the anatomical object of interest at the deformed state is mapped to the deformed pre-operative model to generate a deformed, texture-mapped pre-operative model of the anatomical object of interest.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: Systems and methods are provided for acquiring a series of angiographic images and identifying the anatomical structures represented in the series of images using a machine learnt classifier. Additional series of images that would yield the optimal visualization of the structure of interest may be suggested.

Abstract: A method and system for intelligent automated holistic health management of an individual is disclosed. Medical data for the individual is acquired. A computational lifelong physiology model of the individual is updated based on the acquired medical data. A current health state of the individual is determined using the updated lifelong physiology model of the individual. A holistic health management plan for the individual is generated based on the current health state of the individual using a trained intelligent artificial agent.

Abstract: A method and system for deep learning based cardiac electrophysiological model personalization is disclosed. Electrophysiological measurements of a patient, such as an ECG trace, are received. A computational cardiac electrophysiology model is personalized by calculating patient-specific values for a parameter of the computational cardiac electrophysiology model based at least on the electrophysiological measurements of the patient using a trained deep neural network (DNN). The parameter of the computational cardiac electrophysiology model corresponds to a spatially varying electrical cardiac tissue property.

Abstract: A method and system for personalized blood flow modeling based on wearable sensor networks is disclosed. A personalized anatomical model of vessels of a patient is generated based on initial patient data. Continuous cardiovascular measurements of the patient are received from a wearable sensor network on the patient. A computational blood flow model for simulating blood flow in the patient-specific anatomical model of the vessels of the patient is personalized based on the continuous cardiovascular measurements from the wearable sensor network. Blood flow and pressure in the patient-specific anatomical model of the vessels of the patient are simulated using the personalized computational blood flow model. Hemodynamic measures of interest for the patient are computed based on the simulated blood flow and pressure.

Abstract: A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

Abstract: For personalized modeling with regular integration from a sensor, a wearable sensor and/or sensor outside of the medical facility or environment provides health-related data on a regular, periodic, or continuous basis (e.g., every few minutes or hours). Rather than using that data alone, the data is used to update a previously created personalized model of anatomy of the patient. After updating a parameter value for the personalized model, the updated model is used to output more complex health-related information than provided by the sensors.

Abstract: Methods and systems for estimating patient-specific cardiac electrical properties from medical image data and non-invasive electrocardiography measurements of a patient are disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. Patient-specific cardiac electrical properties are estimated by simulating cardiac electrophysiology over time in the patient-specific anatomical heart model using a computational cardiac electrophysiology model and adjusting cardiac electrical parameters based on the simulation results and the non-invasive electrocardiography measurements. A patient-specific cardiac electrophysiology model with the patient-specific cardiac electrical parameters can then be used to perform virtual cardiac electrophysiology interventions for planning and guidance of cardiac electrophysiology interventions.

Abstract: Using computational models for the patient physiology and the various therapy options, a decision support system presents a range of predicted outcomes to assist in planning the therapy. The models are used in various experiments for the many therapy options to determine an optimal approach.

Abstract: A method and system for simulating patient-specific cardiac electrophysiology including the effect of the electrical conduction system of the heart is disclosed. A patient-specific anatomical heart model is generated from cardiac image data of a patient. The electrical conduction system of the heart of the patient is modeled by determining electrical diffusivity values of cardiac tissue based on a distance of the cardiac tissue from the endocardium. A distance field from the endocardium surface is calculated with sub-grid accuracy using a nested-level set approach. Cardiac electrophysiology for the patient is simulated using a cardiac electrophysiology model with the electrical diffusivity values determined to model the Purkinje network of the patient.

Abstract: A system and method for visualization of cardiac changes under various pacing conditions for intervention planning and guidance is disclosed. A patient-specific anatomical heart model is generated based on medical image data of a patient. A patient-specific computational model of heart function is generated based on patient-specific anatomical heart model. A virtual intervention is performed at each of a plurality of positions on the patient-specific anatomical heart model using the patient-specific computational model of heart function to calculate one or more cardiac parameters resulting from the virtual intervention performed at each of the plurality of positions. One or more outcome maps are generated visualizing, at each of the plurality of positions on the patient-specific anatomical heart model, optimal values for the one or more cardiac parameters resulting from the virtual intervention performed at the that position on the patient-specific anatomical heart model.

Abstract: A method and system for simulating blood flow in a vessel of a patient to estimate hemodynamic quantities of interest using enhanced blood flow computations based on invasive physiological measurements of the patient is disclosed. Non-invasive patient data including medical image data is received and a patient-specific anatomical model the patient's vessels is generated. Invasive physiological measurements of the patient are received and a computational blood flow model is personalized using the invasive physiological measurements. Blood flow is simulated in the patient-specific anatomical model and one or more hemodynamic quantities of interest are computed using the personalized computational blood flow model.

Abstract: A method and system for patient-specific planning and guidance of electrophysiological interventions is disclosed. A patient-specific anatomical heart model is generated from cardiac image data of a patient. A patient-specific cardiac electrophysiology model is generated based on the patient-specific anatomical heart model and patient-specific electrophysiology measurements. Virtual electrophysiological interventions are performed using the patient-specific cardiac electrophysiology model. A simulated electrocardiogram (ECG) signal is calculated in response to each virtual electrophysiological intervention.

Abstract: A method and system for patient-specific simulation of cardiac electrophysiology is disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific cardiac electrophysiology model is generated based on simulated torso potentials and body surface potential measurements of the patient. Cardiac electrophysiology of the patient is simulated over time for the patient-specific anatomical heart model using the patient-specific electrophysiology model. One or more electrophysiology maps are generated based on the cardiac electrophysiology simulated using the patient-specific cardiac electrophysiology model.