Abstract: Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Abstract: A unitary deflection member. The unitary deflection member can have a backside defining an X-Y plane and a thickness in a Z-direction. The unitary deflection member can also have a reinforcing member and a plurality of protuberances positioned on the reinforcing member. Each protuberance can have a three-dimensional shape such that any cross-sectional area of the protuberance parallel to the X-Y plane can have an equal or greater area than any cross-sectional area of the protuberance being a greater distance from the X-Y plane in the Z-direction.

Abstract: Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Abstract: Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Abstract: Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Abstract: A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Abstract: A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Abstract: A system for noninvasively determining at least one physiological characteristic of a patient may include at least one computer system configured to, using a three-dimensional surface mesh model created using patient-specific imaging data, create a three-dimensional combined surface and volume mesh model, including at least a first model portion that has a different spatial resolution than at least a second model portion. The computer system may be further configured to input the three-dimensional surface and volume mesh model into a fluid simulation system and determine a measurement of the physiological characteristic, using the fluid simulation system.

Abstract: A method and apparatus for ignoring a duplicated alarm in a communications network are described. In one embodiment, at least one alarm message associated with at least one event is received. A determination of whether the at least one event exists in a database is subsequently made. The at least one event is recorded in the database if the at least one event does not exist in the database. Conversely, the at least one alarm message is suppressed if the at least one event exists in the database.

Abstract: Methods are disclosed that include forming selected perforation designs and patterns in web substrates. The perforation designs and patterns can be formed in linear or nonlinear fashion, can extend in the cross direction or the machine direction and can be formed to complement or match an embossed or printed design on the web. The perforation designs and patterns can be formed utilizing various mechanical perforating techniques.

Abstract: A system for noninvasively determining at least one physiological characteristic of a patient may include at least one computer system configured to, using a three-dimensional surface mesh model created using patient-specific imaging data, create a three-dimensional combined surface and volume mesh model, including at least a first model portion that has a different spatial resolution than at least a second model portion. The computer system may be further configured to input the three-dimensional surface and volume mesh model into a fluid simulation system and determine a measurement of the physiological characteristic, using the fluid simulation system.

Abstract: A computer-implemented method for simulating blood flow through one or more coronary blood vessels may first involve receiving patient-specific data, including imaging data related to one or more coronary blood vessels, and at least one clinically measured flow parameter. Next, the method may involve generating a digital model of the one or more coronary blood vessels, based at least partially on the imaging data, discretizing the model, applying boundary conditions to a portion of the digital model that contains the one or more coronary blood vessels, and initializing and solving mathematical equations of blood flow through the model to generate computerized flow parameters. Finally, the method may involve comparing the computerized flow parameters with the at least one clinically measured flow parameter.

Abstract: A monitoring apparatus and related methods is disclosed. The monitoring apparatus includes a banding structure. The banding structure is connectable to a housing. At least one non-wired, conductive element is connected to the banding structure and positioned along a length of the banding structure, wherein the at least one non-wired, conductive element is connected to form a circuit through the housing. A cut-band detection element is in communication with the circuit, wherein the cut-band detection element detects a break in the circuit.

Abstract: An infrared (IR) imaging system is presented. The system includes a cooling chamber associated with a cooler generating a certain temperature condition inside the chamber. The cooling chamber has an optical window, and includes thereinside an IR detection unit including one or more detectors thermally coupled to the cooler and at least two cold shields thermally coupled to the cooler and carrying at least two imaging optical assemblies. The at least two imaging optical assemblies are enclosed by the cold shields in between the detection unit and the optical window and thereby define at least two different optical channels for imaging light from the optical window onto the one or more detectors of the detection unit.

Abstract: A system for robotic device control and data acquisition, comprising a robotic device control system adapted to receive sensor-based data comprising physical object information, the sensor-based data being received from a plurality of sources, each source comprising at least a physical sensor in communication with the robotic device control system computer via a communications network, an enhanced robotic device control application adapted to receive sensor-based data from the robotic device control system computer and manipulate the sensor-based data to produce enhanced data, and a method for robotic device control and data acquisition.

Abstract: A computer-implemented method for simulating blood flow through a heart valve may first involve receiving patient-specific data, including imaging data related to the heart valve, an inflow tract of the heart valve and an outflow tract of the heart valve, and at least one clinically measured flow parameter. Next, the method may involve generating a digital model of the heart valve and the inflow and outflow tracts, based at least partially on the imaging data, discretizing the model, applying boundary conditions to a portion of the digital model that contains the heart valve and the inflow and outflow tracts, and initializing and solving mathematical equations of blood flow through the model to generate computerized flow parameters. Finally, the method may involve comparing the computerized flow parameters with the at least one clinically measured flow parameter.

Abstract: Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

Abstract: A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Abstract: A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Abstract: A mandrel cupping assembly for releasably engaging unsupported ends of a plurality of mandrels is disclosed. The mandrel cupping assembly comprises a cupping arm turret having a cupping arm turret central axis, a mandrel cup cooperatively associated with each mandrel of the plurality of mandrels, and a first actuator.