System and method for detecting a health condition using eye images

A computer-implemented system for predicting whether a user has a target health condition using eye images, the system comprising:

• at least one processor;
• memory in communication with the at least one processor, and
• software code stored in the memory, which when executed by the at least one processor causes the system to:
• provide automated guidance to the user to obtain, using a computing device operated by the user, a plurality of images including the user's sclera, each of the images corresponding to a guided direction of the user's gaze;
• receive the images from the computing device by way of a network;
• verify that the images sufficiently show the user's sclera, including by estimating a direction of the user's gaze and confirming that the estimated direction for a given one of the images conforms with the guided direction corresponding to that image;
• generate feature-enhanced image data by:
• extracting an eye image of the user from at least one image from the plurality of images;
• masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing;
• and
• applying an autoencoder to enhance features corresponding to the user's sclera in the at least one image to generate the feature-enhanced image data;
• and
• compute a prediction of whether the user has the target health condition by providing the feature-enhanced image data to a convolutional neural network (CNN), wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user's sclera and outside of the masked iris area in the extracted eye image in the at least one image, wherein the target health condition is related to coronavirus, and the CNN comprises a plurality of layers including a convolutional layer, an non-linear activation layer and a max-pooling layer.

A non-transitory computer-readable medium having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to perform:

• providing automated guidance to a user to obtain, using a computing device operated by the user, a plurality of images including the user's sclera, each of the images corresponding to a guided direction of the user's gaze;
• receiving the images from the computing device by way of a network;
• verifying that the images sufficiently show the user's sclera, including by estimating a direction of the user's gaze and confirming that the estimated direction for a given one of the images conforms with the guided direction corresponding to that image;
• generating feature-enhanced image data by:
• extracting an eye image of the user from at least one image from the plurality of images;
• masking an iris area of the extracted eye image of the user in the at least one image such that the iris area of the extracted eye image is absent from downstream processing;
• and
• applying an autoencoder to enhance features corresponding to the user's sclera in the at least one image to generate the feature-enhanced image data;
• and
• computing a prediction of whether the user has the target health condition by providing the feature-enhanced image data to a convolutional neural network (CNN), wherein the feature-enhanced image data comprises image data representing ocular manifestations in the user's sclera and outside of the masked iris area in the extracted eye image in the at least one image, wherein the target health condition is related to coronavirus, and the CNN comprises a plurality of layers including a convolutional layer, an non-linear activation layer and a max-pooling layer.