Caption Health, Inc.

Bay Labs, Inc.

Nicolas Poilvert

Michael G Cannon

Kilian Koepsell

Ha Hong

Charles Cadieu

Johan Mathe

Nathanael Romano

Natalia Bilenko

Nina Miolane

Charles Chen

Nripesh Parajuli

Ali Chaudhry

Charles Frye

Ching Hsieh

Samuel Surette

Provided herein are platforms, systems, and methods for providing ultrasound imaging guidance for automatic evaluation of image quality over a plurality of sub-views.

Systems and methods for volume-based ultrasound guidance and image analysis

An ultrasound guidance dynamic progression method includes selecting a predetermined ultrasound diagnostic workflow in memory of an ultrasound diagnostic computing system, the workflow including a sequence of views of a target organ. The method further includes selecting a first view in the sequence and presenting guidance in a display of the computing system for the first view, for instance visual, audible or haptic feedback. The method yet further includes acquiring imagery in the computing system in association with the first view, and detecting an anomalous feature of the acquired imagery. Finally, the method includes selecting a different view in the sequence as a substitute for a next one of the views in the sequence in response to having detected the anomalous feature, and further presenting different guidance in the display for the different view in lieu of guidance for the next one of the views.

Mid-procedure view change for ultrasound diagnostics

Embodiments of the present invention provide a method, system and computer program product for ultrasound image acquisition optimization according to different respiration modes. A method for ultrasound image acquisition optimization according to different respiration modes includes acquiring by an ultrasound imaging device, an ultrasound image of a target organ. The method further includes comparing attributes of the acquired ultrasound image to association data in a data store of associations associating attributes of previously acquired ultrasound imagery of different images of the target organ with different modes of respiration. Finally, the method includes determining from the comparison, a mode of respiration evident from the acquired ultrasound image and presenting the determined mode in the ultrasound imaging device.

Ultrasound image acquisition optimization according to different respiration modes

A method for clip selection includes receiving through an interface to a medical imaging device, a selection of a diagnostic procedure and a target portion of a mammalian body. Thereafter, the device acquires video clip imagery of the target portion and stores the video clip imagery in an image store. Each video clip of the video clip imagery is then image processed to determine a view and a quality of each video clip and a rule is retrieved from a rules base corresponding to the selected diagnostic procedure and target portion. In this regard, the rule specifies a requisite view and quality of the video clip imagery. Finally, the retrieved rule is applied to the video clip imagery as a filter to produce a subset of video clip imagery satisfying the specified requisite view and quality and the subset of video clip imagery is stored in the image store.

Video clip selector for medical imaging and diagnosis

A saliency mapping method includes displaying video clip imagery of an organ in a display of a computer and submitting the imagery to a neural network trained to produce a probability of an existence of a physical feature of the organ. In response, the probability is received along with a pixel-wise mapping of dispositive pixels in the imagery resulting in the probability. Variations of the imagery are then repeatedly resubmitted to the neural network, each including a change to one or more of the pixels in the imagery. Thereafter, for each resubmission, a change is measured in the probability and then correlated to the changed pixels. Finally, a graphical indicator is overlain on the display of the imagery corresponding to each of the pixels determined through the repeated resubmission to be dispositive based upon a threshold measured change in probability.

Saliency mapping of imagery during artificially intelligent image classification

Embodiments of the present invention provide a method, system and computer program product for video clip quality determination in medical device imaging and diagnosis. In an embodiment of the invention, a method for video clip quality determination in medical imaging includes first retrieving from a data store into memory of a host computer, video clip imagery of a target organ. Then, a quality value assigned to the video clip imagery may be identified in connection with the retrieved video clip imagery. A measurement of the target organ is computed based upon the retrieved video clip imagery. Finally, a confidence in respect to the computed measurement is determined based upon the identified quality value and the confidence determination displayed in a user interface provided by the host computer.

Confidence determination in a medical imaging video clip measurement based upon video clip image quality

Embodiments of the invention provide a method, system and computer program product for artificially intelligent ejection fraction determination. In a method for artificially intelligent ejection fraction determination, a neural network is loaded into memory of a computer, that has been trained with different sets of cardiac imaging data acquired during imaging of a ventricle for different hearts and a known ejection fraction for each of the sets. Then, a contemporaneous set of imaging data is acquired of a ventricle of a heart and the contemporaneous set of imaging data is provided to the neural network. Finally, an ejection fraction determination output by the neural network is displayed in a display of the computer without tracing a ventricle boundary of the heart.

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Seattle, WA, United States of America

Nicolas Poilvert