Abstract: An iterative framework for learning multimodal mappings tailored to medical image inferencing tasks is provided. In an example, a computer-implemented method can comprise receiving multimodal annotation data for medical images, the multimodal annotation data comprising non-image annotation data and image annotation data, and employing one or more machine learning (ML) processes to learn bi-directional mappings between non-image features included in the non-image annotation data and image features associated with the medical images and the image annotation data. The method further comprises generating, as a result of the one or more ML processes, a model configured to: infer one or more of the non-image features associated with new medical images given the new medical images, and/or infer one or more of the image features associated with the new medical images given the new medical images and non-image input corresponding to at least some of the non-image annotation data.

Abstract: Systems are provided for perioperative care. In an example, a system includes one or more processors and memory storing instructions executable by the one or more processors to output a graphical user interface (GUI) including a first visual representation of a default or previously-determined phase of anesthesia delivery for a patient; receive a plurality of monitoring parameters of the patient over an observation window, at least a portion of the plurality of monitoring parameters obtained from an anesthesia delivery machine; identify, by applying a selected set of rules to the plurality of monitoring parameters, whether an event signaling a change to a new phase of anesthesia delivery for the patient is detected; based on the event being detected, update the GUI to display a second visual representation of the new phase; and based on the event not being detected, maintain the first visual representation on the GUI.

Abstract: Techniques are described for computer-implemented techniques for managing various aspects of the cardiac care pathway using machine learning. According to an embodiment, a method can include training an outcomes forecasting model to predict patient outcomes resulting from undergoing a cardiac valve procedure using multi-modal training data for a plurality of different patients, wherein the training comprising separately training different machine learning sub-models of the forecasting model to predict preliminary patient outcome data and mapping the preliminary patient outcome data to the patient outcomes, resulting in a trained version of the outcome forecasting model. The method further includes applying the trained version of the outcomes forecasting model to new multi-modal data for a new patient to predict the patient outcomes for the new patient resulting from undergoing the cardiac value procedure.

Abstract: Methods and systems are provided for generating respiratory support recommendations. In one embodiment, a method includes extracting imaging features from patient imaging information for a patient, extracting non-imaging features from patient clinical data of the patient, entering the imaging features and the non-imaging features to a joint model trained to output respiratory support recommendations as a function of the imaging features and the non-imaging features, and displaying one or more respiratory support recommendations output by the joint model.

Abstract: Systems and methods are provided for super resolution for electronic 4D (e4D) cardiovascular ultrasound (CVUS) probes. In a medical imaging system, signals associated with a medical imaging technique may be acquired and processed, with the processing including applying one or both of a first type of correction to address a first type of degradation and a second type of correction to address a second type of degradation, with the first type of degradation being based on or caused by sparse acquisition, and the second type of degradation being based on or caused by choice of beamforming/reconstruction methodology.

Abstract: A method of removing a metal-containing layer (e.g., tungsten) from a substrate is provided. The method includes generating a first plasma in a process volume of a plasma chamber when a patterned device is disposed on a substrate support in the process volume. The patterned device includes a patterned region and an unpatterned region; a substrate; a tungsten-containing layer formed over the substrate; a supporting layer disposed between the tungsten-containing layer and the substrate. The patterned region includes exposed surfaces of the supporting layer and the unpatterned region does not include any exposed surfaces of the supporting layer. The method further includes depositing a first film over the patterned region of the tungsten-containing layer with the first plasma; and removing portions of the unpatterned region of the tungsten-containing layer with the first plasma without depositing the first film over the unpatterned region.

Abstract: A system and method for automatically adjusting beamformer parameters based on ultrasound image analysis to enhance ultrasound image acquisition is provided. The method includes acquiring, by an ultrasound system, an ultrasound image. The method includes segmenting, by at least one processor, the ultrasound image to identify anatomical structure(s) and/or image artifact(s) in the ultrasound image. The method includes detecting, by the at least one processor, a location of each of the identified anatomical structure(s) and/or image artifact(s). The method includes automatically adjusting, by the at least one processor, at least one beamformer parameter based on the detected location of one or more of the identified anatomical structure(s) and/or the image artifact(s). The method includes acquiring, by the ultrasound system, an enhanced ultrasound image based on the automatically adjusted at least one beamformer parameter. The method includes presenting, at a display system, the enhanced ultrasound image.

Abstract: The disclosure relates generally to a patient monitoring device and, more particularly, to improved system and method to detect a false alarm in a patient monitoring device. The disclosure specifically relates to a system and a method to detect a false alarm in a patient monitoring device. The system may include a patient monitoring device configured to receive a patient monitoring data from a patient. The system may enable the processing of the patient monitoring data by a processing device to determine a false alarm generated by the patient monitoring device. The system may further provide a user-interface, which may be configured to filter a true alarm from a false alarm generated by the patient monitoring device.

Abstract: Systems/techniques that facilitate smart training and smart deployment of machine learning models are provided. In various embodiments, a system can access a first set of data candidates that are available for training of a machine learning model. In various aspects, the system can compute at least one feature distribution of the first set of data candidates. In various instances, the system can identify, in the first set of data candidates, a strict subset of data candidates, wherein at least one feature distribution of the strict subset of data candidates matches the at least one feature distribution of the first set of data candidates. In various cases, the system can train the machine learning model on the strict subset of data candidates.

Abstract: Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

Abstract: Systems and methods are provided for perioperative care in a medical facility. In an example, a system includes a display and a computing device operably coupled to the display and storing instructions executable to output, to the display, a graphical user interface (GUI) that includes real-time medical device data of a patient, at least some of the real-time medical device data displayed via the GUI as a plurality of patient monitoring parameter tiles, the GUI including a risk score indicative of a relative likelihood that the patient will exhibit a condition within a period of time, and responsive to a user input, display, on the GUI, a set of trend lines each showing values for a respective patient monitoring parameter over a time range, each trend line of the set of trend lines selected based on a contribution of each respective patient monitoring parameter to the risk score.

Abstract: Techniques are described that that facilitate securely deploying artificial intelligence (AI) models and distributing inferences generated therefrom. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise an algorithm execution component that applies an AI model to input data and generates output data, and an encryption component that encrypts the output data using a proprietary encryption mechanism, resulting in encrypted output data. The proprietary encryption mechanism can include a mechanism that prevents usage and rendering of the encrypted output data without decryption of the encrypted output data using a proprietary decryption mechanism.

Abstract: Systems and techniques for monitoring, predicting and/or alerting for short-term oxygen support needs of patients are presented. A system can include a data collection component that receives multimodal patient data for a patient having a respiratory condition in association with monitoring and treating the respiratory condition in real-time, the multimodal patient data comprising at least physiological data regarding physiological parameters tracked for the patient over a period of time, and current oxygen support data regarding a current oxygen support mechanism of the patient.

Abstract: Sub-resolution measurement of fuel in fuel tank. In an embodiment, data, which comprise discrete fuel levels in a fuel tank and fuel injection rates for an internal combustion engine, are received. The fuel injection rates are integrated over a traversed distance to produce a fuel consumption series, and the discrete fuel levels are clustered over the traversed distance to produce a fuel level series. The fuel consumption and fuel level series are synchronized into a model that is used to generate sub-resolution measurements of fuel levels between the discrete fuel levels.

Abstract: A method of removing a metal-containing layer (e.g., tungsten) from a substrate is provided. The method includes generating a first plasma in a process volume of a plasma chamber when a patterned device is disposed on a substrate support in the process volume. The patterned device includes a patterned region and an unpatterned region; a substrate; a tungsten-containing layer formed over the substrate; a supporting layer disposed between the tungsten-containing layer and the substrate. The patterned region includes exposed surfaces of the supporting layer and the unpatterned region does not include any exposed surfaces of the supporting layer. The method further includes depositing a first film over the patterned region of the tungsten-containing layer with the first plasma; and removing portions of the unpatterned region of the tungsten-containing layer with the first plasma without depositing the first film over the unpatterned region.

Abstract: Sub-resolution measurement of fuel in fuel tank. In an embodiment, data, which comprise discrete fuel levels in a fuel tank and fuel injection rates for an internal combustion engine, are received. The fuel injection rates are integrated over a traversed distance to produce a fuel consumption series, and the discrete fuel levels are clustered over the traversed distance to produce a fuel level series. The fuel consumption and fuel level series are synchronized into a model that is used to generate sub-resolution measurements of fuel levels between the discrete fuel levels.

Abstract: A method of removing a metal-containing layer (e.g., tungsten) from a substrate is provided. The method includes generating a first plasma in a process volume of a plasma chamber when a patterned device is disposed on a substrate support in the process volume. The patterned device includes a patterned region and an unpatterned region; a substrate; a tungsten-containing layer formed over the substrate; a supporting layer disposed between the tungsten-containing layer and the substrate. The patterned region includes exposed surfaces of the supporting layer and the unpatterned region does not include any exposed surfaces of the supporting layer. The method further includes depositing a first film over the patterned region of the tungsten-containing layer with the first plasma; and removing portions of the unpatterned region of the tungsten-containing layer with the first plasma without depositing the first film over the unpatterned region.

Abstract: Methods and systems are provided for generating respiratory support recommendations. In one embodiment, a method includes extracting imaging features from patient imaging information for a patient, extracting non-imaging features from patient clinical data of the patient, entering the imaging features and the non-imaging features to a joint model trained to output respiratory support recommendations as a function of the imaging features and the non-imaging features, and displaying one or more respiratory support recommendations output by the joint model.

Abstract: A method of removing a metal-containing layer (e.g., tungsten) from a substrate is provided. The method includes generating a first plasma in a process volume of a plasma chamber when a patterned device is disposed on a substrate support in the process volume. The patterned device includes a patterned region and an unpatterned region; a substrate; a tungsten-containing layer formed over the substrate; a supporting layer disposed between the tungsten-containing layer and the substrate. The patterned region includes exposed surfaces of the supporting layer and the unpatterned region does not include any exposed surfaces of the supporting layer. The method further includes depositing a first film over the patterned region of the tungsten-containing layer with the first plasma; and removing portions of the unpatterned region of the tungsten-containing layer with the first plasma without depositing the first film over the unpatterned region.