Abstract: A hybrid inference facility receives a sequence of data items. For each data item, the facility: forwards the data item to a server; subjects it to a local machine learning model to produce a local inference result for the data item; and the local inference result to a queue; aggregates the inference results contained by the queue to obtain an output inference result; and removes the oldest inference result from the queue. The facility receives from the server cloud inference results each obtained by applying a server machine learning model to one of the data items forwarded to the server. For each received cloud inference result, the facility substitutes the cloud inference result in the queue for the local inference result for the same data item.

Abstract: A machine learning model is described that is trained without labels to predict a motion field between a pair of images. The trained model can be applied to a distinguished pair of images to predict a motion field between the distinguished pair of images.

Abstract: Devices and methods in which auscultation signals, ultrasound signals, and ambient noise signals may be simultaneously acquired by a same handheld device are provided. Moreover, in some embodiments, electrocardiogram (EKG) may be acquired by the handheld device. One such device includes a housing having a sensor portion at a distal end of the housing, and a handle portion between a proximal end and the distal end of the housing. An ultrasound sensor is positioned at least partially within the sensor portion of the housing, and a first auscultation sensor is positioned at least partially within the sensor portion of the housing. An ambient noise sensor is positioned at least partially within the housing between the handle portion and the proximal end of the housing.

Abstract: Systems, methods and devices for providing combined ultrasound, electrocardiography, and auscultation data are provided. One such system includes an ultrasound sensor, an electrocardiogram (EKG) sensor, an auscultation sensor, and a computing device. The computing device includes memory and a processor, and the processor receives signals from the ultrasound sensor, the EKG sensor, and the auscultation sensor. Artificial intelligence techniques may be employed for automatically analyzing the data obtained from the ultrasound sensor, the EKG sensor, and the auscultation sensor and producing a clinically-relevant determination based on a combined analysis of the data.

Abstract: A facility identifies anatomical objects visualized by a medical imaging image. The facility applies two machine learning models to the image: a first trained to predict a view probability vector that, for each of a list of views, attributes a probability that the image was captured from the view, and a second trained to predict an object probability vector that, for each of a list of anatomical objects, attributes a probability that the object is visualized by the image. For each object, the facility: (1) accesses a list of views in which the object is permitted; (2) multiplies the predicted probability that the object is visualized by the image by the sum of the predicted probabilities that the accessed image was captured from views in which the object is permitted; and (3) where the resulting probability exceeds a threshold, determines that the object is visualized by the accessed image.

Abstract: A facility for processing a medical imaging image is described. The facility applies each of a number of constituent models making up an ensemble machine learning models to the image to produce a constituent model result that predicts a value for each pixel of the image. The facility aggregates the results produced by the constituent models of the plurality to determine a result of the ensemble machine learning model. For each of the pixels of the accessed image, the facility determines a measure of variation among the values predicted for the pixel among the constituent models. Facility determines a confidence measure for the ensemble machine learning model result based at least in part on for how many of the pixels of the accessed image a variation measure is determined that exceeds a variation threshold.

Abstract: A facility for processing a medical imaging image is described. The facility applies to the image a first machine learning model trained to recognize a view to which an image corresponds, and a second machine learning model trained to identify any of a set of anatomical features visualized in an image. The facility accesses a list of permitted anatomical features for images corresponding to the recognized view, and filters the identified anatomical features to exclude any not on the accessed list. The facility causes the accessed image to be displayed, overlaid with a visual indication of each of the filtered identified anatomical features.

Abstract: Flex circuits and methods for ultrasound transducers are provided herein. In at least one embodiment, an ultrasound device includes a plurality of transducer elements and a flex circuit. The flex circuit includes an insulating layer having a first surface and a second surface opposite the first surface. A plurality of first conductive pads is included on the first surface of the insulating layer, and each of the first conductive pads is electrically coupled to a respective transducer element. A plurality of second conductive pads are included on the second surface of the insulating layer, and each of the second conductive pads is electrically coupled to a respective first conductive pad and the respective transducer element.

Abstract: Ultrasound image recognition systems and methods, and artificial intelligence training networks for such systems and methods, are provided. An ultrasound data information system includes an ultrasound image recognition training network that is configured to receive ultrasound training images and to develop ultrasound image knowledge based on the received ultrasound training images. An ultrasound imaging device acquires ultrasound images of a patient, and the device includes an ultrasound image recognition module. The ultrasound image recognition module is configured to receive the ultrasound image knowledge, receive the acquired ultrasound images from the ultrasound imaging device, and determine, based on the ultrasound image knowledge, whether the received ultrasound images represent a clinically desirable view of an organ or whether the clinically desirable views indicate normal function or a particular pathology.

Abstract: Systems, methods and devices for providing combined ultrasound, electrocardiography, and auscultation data are provided. One such system includes an ultrasound sensor, an electrocardiogram (EKG) sensor, an auscultation sensor, and a computing device. The computing device includes memory and a processor, and the processor receives signals from the ultrasound sensor, the EKG sensor, and the auscultation sensor. Artificial intelligence techniques may be employed for automatically analyzing the data obtained from the ultrasound sensor, the EKG sensor, and the auscultation sensor and producing a clinically-relevant determination based on a combined analysis of the data.

Abstract: Circuits, devices and methods generating an ultrasound pulse are provided herein. An ultrasound probe includes an array of transducer elements configured to transmit an ultrasound signal toward a target structure in a region of interest, and an array of pulse generators for driving a respective transducer element. Each of the pulse generators are coupled to a respective transducer element and include a driver circuit configured to receive a control signal and to output a driving signal, a switching element coupled to the driver circuit, and a resonant circuit coupled to the switching element and the respective transducer element, wherein the resonant circuit is configured to provide a transmit pulse to the respective transducer element based on the driving signal. The switching element may comprise a gallium nitride field effect transistor (GaN FET).

Abstract: An ultrasound system comprising a portable ultrasound probe and a docking station provides enhanced ultrasound functionality. One or more first transducer elements transmit an ultrasound signal toward a target structure in a region of interest. Processing circuitry controls transmission of the ultrasound signal, while driving circuitry that is operatively coupled to the one or more first transducer elements and the processing circuitry drives the transmission of the ultrasound signal. The ultrasound probe further includes one or more second transducer elements that receive echo signals returning from the target structure in response to transmission of the ultrasound signal, and a power supply that provides power to the driving circuitry for transmission of the ultrasound signal. The docking station includes an interface that is couplable to the ultrasound probe and circuitry that electrically couples to the ultrasound probe via the interface and enhances the ultrasound functionality of the ultrasound probe.

Abstract: A method of determining a status of ultrasound coupling medium for performing an ultrasound scan for providing an ultrasound image including plural scanlines (Nl) is disclosed. In an embodiment, the method includes operating an ultrasound device to capture an image frame including plural scanlines (Nl), each scanline having an associated sample set (s) of intensity values; processing a subset of the associated sample set (s) of values for each scanline to determine a first summation for each scanline; processing plural sets of corresponding intensity values from each of plural scanlines located within a range of a respective scanline to determine a set of difference values for each respective scanline; processing each set of difference values to determine a second summation for each scanline; and generating a status for the ultrasound coupling medium according to a relationship between each of the first summations and each of the associated second summations.

Abstract: Systems, methods and devices for providing combined ultrasound, electrocardiography, and auscultation data are provided. One such system includes an ultrasound sensor, an electrocardiogram (EKG) sensor, an auscultation sensor, and a computing device. The computing device includes memory and a processor, and the processor receives signals from the ultrasound sensor, the EKG sensor, and the auscultation sensor. Artificial intelligence techniques may be employed for automatically analyzing the data obtained from the ultrasound sensor, the EKG sensor, and the auscultation sensor and producing a clinically-relevant determination based on a combined analysis of the data.