University of Miami

Amr Saad Mohamed Elsawy

Mohamed Abou Shousha

In some embodiments, initial feedback indicating threshold characteristics (under which a user sees initial stimuli presented on a user interface) may be provided to a prediction model, and a set of predicted characteristics (for a set of locations of the user interface) and a set of confidence scores associated with the set of locations may be obtained via the prediction model. Based on the set of confidence scores, one or more locations may be selected to be tested during a visual test presentation. As an example, the locations may be selected over one or more other locations of the set of locations based on the set of confidence scores. Based on predicted characteristics associated with the selected locations, stimuli may be presented at the selected locations during the visual test presentation. Visual defect information for the user may be generated based on feedback from the visual test presentation.

Vision testing via prediction-based setting of an initial stimuli characteristic for user interface locations

Vision testing via prediction-based setting of initial stimuli characteristics for user interface locations

Vision testing via prediction-based setting of an initial stimuli characteristic for a user interface location

In some embodiments, a set of eye images related to a subject may be provided to a prediction model. A first prediction may be obtained via the prediction model, where the first prediction is derived from a first eye image and indicates whether an eye condition is present in the subject. A second prediction may be obtained via the prediction model, where the second prediction is derived from a second eye image and indicates that the eye condition is present in the subject. An aspect associated with the first prediction may be adjusted via the prediction model based on the second prediction's indication that the eye condition is present in the subject. One or more predictions related to at least one eye condition for the subject may be obtained from the prediction model, where the prediction model generates the predictions based on the adjustment of the first prediction.

System and method for AI-based eye condition determinations

A method of predicting a disease or condition of a cornea or an anterior segment of an eye includes inputting input data into an AI model, processing the input data, and generating a set of scores and outputting a prediction. The input data may be representative of a cornea or anterior segment of an eye. Processing the input data may include processing the data through the plurality of convolutional layers, the fully connected layer, and the output layer. Each score of the set of scores may be generated by a corresponding node in the output layer. The output prediction may be related to the cornea or anterior segment of the eye represented by the input data processed through the AI model. The prediction may be determined by at least one score of the set of scores.

Artificial intelligence-based system and methods for corneal diagnosis

Techniques for improved diagnosis, treatment, and monitoring of corneal pathologies use enhanced mapping of the cornea or corneal regions, to develop three-dimensional mapping of corneal thickness, while retaining particular corneal micro-layer thickness data. Anterior and posterior surface identifications, along with surface apex determinations, are used for registration of segmentation of these micro-layers. 3D heat maps and bull's-eye maps are generated from resulting thickness date. The maps provided enhanced evaluation and diagnosis of a corneal pathologies, such as keratoconus, pellucid marginal degeneration, post-refractive surgery ectasia, keratoglobus, corneal transplant rejection and corneal transplant failed grafts, Fuchs dystrophy, corneal limbal stem cell deficiency, dry eye syndrome, and post-corneal collagen crosslinking evaluation.

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Miami, FL, United States of America

Amr Saad Mohamed Elsawy