Digital Diagnostics Inc.

Warren Clarida

Michael D Abramoff

Meindert Niemeijer

Jacob Patrick Suther

Abhay Shah

Ryan Earl Rohret Amelon

Ben Clark

Eric Talmage

John Casko

Timothy Dinolfo

Tay Stutts

Ryan Amelon

Systems and methods are provided herein for minimizing retinal exposure to flash during image gathering for diagnosis. In an embodiment, a system captures a plurality of retinal images of different retinal regions. The system determines that a first portion of a first image does not meet a criterion while a second portion of the first image does meet the criterion, identifies a portion of the retina depicted in the first portion that does not meet the criterion, and determines whether the portion of the retina is depicted in a third portion of a second image and whether the third portion meets the criterion. Responsive to determining that the third portion meets the criterion, the system performs the diagnosis. Responsive to determining that the portion of the retina is not depicted in the second image, the system captures an additional image of the retinal region.

Image retention and stitching for minimal-flash eye disease diagnosis

Disclosed is a system for qualifying medical images submitted by user for diagnostic analysis comprising: an image input module, configured to receive one or more image input by a user; an image protocol conformation module, configured to receive the one or more images from the image input module, and further configured to analyze each of the one or more images for conformity with a predefined protocol and wherein images that do not conform to the predefined protocol are flagged as non-conforming images; an image output module, configured to identify to the user each of the one or more images flagged as non-conforming and prompting the user to resubmit a new image for each of the non-conforming images; and an image resubmission module, configured to receive the user resubmitted image and provide the resubmitted image to the image protocol conformation module.

System and methods for qualifying medical images

Systems and methods are disclosed herein for detecting eye alignment during retinal imaging. In an embodiment, the system receives an infrared stream from an imaging device, the infrared stream showing characteristics of an eye of a patient. The system determines, based on the infrared stream, that the eye is improperly aligned at a first time, and outputs sensory feedback indicative of the improper alignment. The system detects, based on the infrared stream at a second time later than the first time, that the eye is properly aligned, and receives an image of a retina of the properly aligned eye from the imaging device.

Using infrared to detect proper eye alignment before capturing retinal images

Systems and methods are disclosed herein for adjusting flash intensity based on retinal pigmentation. In an embodiment, a processor determines a retinal pigmentation of a retina of an eye positioned at an imaging device. The processor commands the imaging device to adjust an intensity of a flash component from a first intensity to a second intensity based on the retinal pigmentation. The processor commands the imaging device to capture an image that is lit by the flash component at the second intensity, and receives the image from the imaging device.

Dynamic adjustment of flash intensity based on retinal pigmentation

A device receives an input image of a portion of a patient's body, and applies the input image to a feature extraction model, the feature extraction model comprising a trained machine learning model that is configured to generate an output that comprises, for each respective location of a plurality of locations in the input image, an indication that the input image contains an object of interest that is indicative of a presence of a disease state at the respective location. The device applies the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on the output of the feature extraction model. The device outputs the determined diagnosis of a disease condition in the patient obtained from the diagnostic model.

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Cedar Rapids, IA, United States of America

Warren Clarida