Abstract: A seamless unitary deflection member. The seamless unitary deflection member can have a backside defining an X-Y plane and a thickness in a Z-direction. The seamless unitary deflection member may also have a reinforcing member and a plurality of protuberances positioned on the reinforcing member. Each protuberance may 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 lesser area than any cross-sectional area of the protuberance being a greater distance from the X-Y plane in the Z-direction.

Abstract: Systems, media, methods, and kits disclosed herein can improve analysis capabilities of genomic materials. Results from such analyses can be used to detect genomic biomarkers in one or more genomic materials. The systems, media, methods and kits disclosed herein can identify changes or patterns among samples, and can employ machine learning methods to explore changes or potential changes in biological conditions or risks thereof. Further, the systems, media, methods and kits disclosed herein can utilize machine learning algorithms to analyze samples with high accuracy.

Abstract: A deflection member that includes a reinforcing member and a plurality of tiles fastened to the reinforcing member.

Abstract: A deflection member that includes a reinforcing member that includes a resin coating, and at least one tile fastened to the resin coating.

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 deflection member that includes a reinforcing member and a plurality of tiles fastened to the reinforcing member.

Abstract: A deflection member. The deflection member can be a unitary structure having a plurality of discrete primary elements and a plurality of secondary elements. At least one of the secondary elements can be an elongate member having a major axis having both a machine direction vector component and a cross machine direction vector component. In an example, the secondary elements can be arranged in a Voronoi pattern.

Abstract: A medical instrument and method is described for facilitating lacrimal occlusion. The instrument has two arms for holding a plug being inserted through the punctum, each arm having a distal end and a proximal end. Attached to the proximal end of the arms is a dilator oriented in the opposite direction along the longitudinal axis of the instrument. The instrument is conveniently rotated in the hand of the practitioner to alternately present the functioning end of the instrument as either the distal end of the arms (for holding a plug) or the distal end of the dilator (for enlarging the punctum prior to attempting to inserting the plug). The instrument has a means for moving the two arms near to each other and away from each, and a means for holding the two arms near to each other without requiring closing pressure applied by the practitioner.

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 deflection member that includes a reinforcing member and a plurality of tiles fastened to the reinforcing member.

Abstract: Systems and methods for estimating the post-treatment ultrafiltration rate of a patient are provided. A medical device can be configured to determine an estimated post-treatment ultrafiltration rate based on one or more values associated with a patient prepared to undergo treatment with the medical device. The medical device can also be configured to compare the estimated post-treatment ultrafiltration rate with one or more threshold values. The medical device can be configured to have an alert module, which can be activated when the estimated post-treatment ultrafiltration rate exceeds the one or more threshold values.

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 deflection member that includes a reinforcing member and a plurality of tiles fastened to the reinforcing member.

Abstract: A deflection member that includes a reinforcing member and a plurality of tiles fastened to the reinforcing member.

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: 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 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: 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 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: Systems and methods are disclosed for estimating patient-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, a geometric model and estimated blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the geometric model and estimated blood flow characteristics for each of the plurality of individuals; identifying, using the machine learning algorithm, features predictive of blood flow characteristics corresponding to a plurality of points in the geometric models; acquiring, for a patient, a geometric model of at least part of the patient's vascular system; and using the identified features to produce estimates of the patient's blood flow characteristic for each of a plurality of points in the patient's geometric model.