Welch Allyn, Inc.

Allen R Hart

Ynjiun P Wang

Eric P Jensen

Thomas A Myers

Richard M Farchione

Scott P Gucciardi

Charles E Witkowski, Ii

Austin Gardner

Salvin J Strods

Hongying Krause

David Lance Ribble

Craig M Meyerson

Christian H Reinke

Carlos Andres Suarez

Stacey A Fitzgibbons

Gene J Wolfe

Su Wang

Heather Whitt

Douglas Paul Fenster

Alexander Dennis Fries

Robert Christopher Rogers

Varad Srivastava

Charles E Witkowski

Software of devices, such as medical devices or other constrained devices configured to implement specific functions, can use content files to determine text strings or other content values to present in association with user interface elements. Devices can receive relevant content files, such as new versions of previously-stored content files or new content files containing text in different languages, from a remote content file repository. The software of the devices can accordingly update content values presented in user interfaces based on newly received content files, without the software being re-coded, the software being restarted, or the devices rebooting.

Remote management of device user interface content

A system for capturing one or more eye images includes an eye imaging device having a camera configured to capture the eye images. The system determines a workflow for capturing the eye images using the eye imaging device. The workflow is determined based on a risk score for a given patient. The system performs the workflow on the eye imaging device to capture the eye images, conducts a microvascular assessment based on the captured eye images, and adjusts the risk score based on the microvascular assessment.

Microvascular assessment using eye imaging device

An example method includes identifying at least one image of an eye of a patient. The method further includes detecting, by a first computing model, at least one first feature in the at least one image and detecting, by a second computing model, at least one second feature in the at least one image. Further, using a third computing model that is different than the first computing model or the second computing model, the method includes identifying a likelihood that the patient has one or more diseases consistent with the at least one feature and the at least one second feature. A recommendation for care of the patient is generated based on the likelihood.

Automated disease identification based on ophthalmic images

A system includes a vision screening device having at least one sensor. The system also includes a controller operably connected to the at least one sensor. The controller is configured to cause the at least one sensor to obtain an image of an eye, and to identify a region of interest associated with a macula of the eye. The controller is also configured to identify drusen disposed proximate the macula, and to generate an augmented image of the eye. The augmented image includes a component indicating the drusen.

Detecting and tracking macular degeneration

An example system for microvascular assessment of a patient can include: a fundus imaging device including: a camera configured to capture one or more images of an eye of the patient; and at least one light source; and a microvascular assessment computing device including: a processor; and memory encoding instructions which, when executed by the processor, cause the system to: activate the light source to direct light at a fundus of the eye of the patient; capture, with the camera, one or more images of the fundus of the patient, the fundus comprising a plurality of blood vessels; and analyze with the microvascular assessment computing device, the one or more images to determine a microvasculature health index for the patient.

Fundus imaging for microvascular assessment

An apparatus for producing a fundus image includes: a processor and a memory; an illumination component including a light source and operatively coupled to the processor; a camera including a lens and operatively coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the apparatus to: execute an automated script for capture of the fundus image; and allow for manual capture of the fundus image.

Retinal image capturing

An example device is configured to capture an image of an eye. The device includes a camera configured to capture the image of the eye. The device also includes: a first base configured to be moved along a first axis to position the camera to capture the image of the eye; a second base configured to be moved along a second axis to position the camera to capture the image of the eye; and a third base configured to be moved along a third axis to position the camera to capture the image of the eye.

Fundus image capturing

An example method for automating a quality assessment of digital fundus image can include: obtaining a digital fundus image file; analyzing a first quality of the digital fundus image file using a model to estimate an optimal time to capture a fundus image; and analyzing a second quality of the digital fundus image file using the model to estimate a disease state.

Assessment of fundus images

A fundus image capture system operates to determine if the system has captured an image that identifies the fundus, a region of interest in the image, or the current field of view. According to the determination, the system can guide the user to operate the system to capture a desired fundus image.

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Knoxville, TN, United States of America

Allen R Hart