Johns Hopkins University

Austin Reiter

Russell H Taylor

Kevin C Olds

Tae Soo Kim

Muyinatu Bell

Suchi Saria

Andy Jinhua Ma

An embodiment according to the present invention includes a method for a machine-learning based approach to the formation of ultrasound and photoacoustic images. The machine-learning approach is used to reduce or remove artifacts to create a new type of high-contrast, high-resolution, artifact-free image. The method of the present invention uses convolutional neural networks (CNNs) to determine target locations to replace the geometry-based beamforming that is currently used. The approach is extendable to any application where beamforming is required, such as radar or seismography.

Machine learning approach to beamforming

An embodiment in accordance with the present invention includes a technology to continuously measure patient mobility automatically, using sensors that capture color and depth images along with algorithms that process the data and analyze the activities of the patients and providers to assess the highest level of mobility of the patient. An algorithm according to the present invention employs the following five steps: 1) analyze individual images to locate the regions containing every person in the scene (Person Localization), 2) for each person region, assign an identity to distinguish 'patient' vs. 'not patient' (Patient Identification), 3) determine the pose of the patient, with the help of contextual information (Patient Pose Classification and Context Detection), 4) measure the degree of motion of the patient (Motion Analysis), and 5) infer the highest mobility level of the patient using the combination of pose and motion characteristics (Mobility Classification).

Measuring patient mobility in the ICU using a novel non-invasive sensor

A system for stereo reconstruction from a monoscopic endoscope includes an image pick-up element at a distal end thereof and a working channel defined by a body of the monoscopic endoscope. The system comprises a light patterning component configured to be disposed within the working channel such that a light emitting end of the light patterning component will be fixed with a defined relative distance from the distal end of the image pick-up element. The system also includes a data processor adapted to be in communication with the image pick-up element. The light patterning component forms a pattern of light that is projected onto a region of interest. The data processor receives an image signal of the region of interest that includes the pattern, and determines a distance from the endoscope to the region of interest based on the image signal and based on the defined relative distance.

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Annapolis, MD, United States of America

Austin Reiter