Abstract: Embodiments of the invention involve combining data representative of the eye obtained from multiple modalities into a virtual model of the eye. The multiple modalities indicate anatomical, physiological, and/or functional features of the eye. The data from different modalities is registered in order to combine the data into the virtual model. Further embodiments involve analysing eye data, for example in the form of the virtual model, using neural networks to obtain insights about medical conditions of the eye, for example the diagnosis or prognosis of conditions, and/or predicting how the eye will respond to certain treatments.

Abstract: Systems and methods for predicting a risk of cardiovascular disease (CVD) from one or more fundus images are disclosed. Fundus images associated with an individual are processed to determine whether fundus images are of sufficient quality. The fundus images of sufficient quality are processed to identify fundus images belonging to a single eye. A plurality of risk contributing factor sets of CNNs (RCF CNN) are configured to output an indicator of probability of the presence of a different risk contributing factor in each of the one or more fundus images. At least one of the RCF CNNs is configured in a jury system model having a plurality of jury member CNNs, each being configured to output a probability of a different feature in the one or more fundus images. The outputs of the jury member CNNs are processed to determine the indicator of probability of the presence of the risk contributing factor output by the RCF CNN.

Abstract: Embodiments of the invention involve combining data representative of the eye obtained from multiple modalities into a virtual model of the eye. The multiple modalities indicate anatomical, physiological, and/or functional features of the eye. The data from different modalities is registered in order to combine the data into the virtual model. Further embodiments involve analysing eye data, for example in the form of the virtual model, using neural networks to obtain insights about medical conditions of the eye, for example the diagnosis or prognosis of conditions, and/or predicting how the eye will respond to certain treatments.

Abstract: Methods and systems for detecting glycosylated haemoglobin (HbA1c) levels from at least one fundus image are disclosed. At least one fundus image associated with an individual is processed using a first set of one or more convolutional neural networks to determine a glycosylated haemoglobin (HbA1c) level for the at least one fundus image. Methods and systems of determining a risk level of progression of diabetic retinopathy of an individual are also disclosed. At least one fundus image associated with the individual is processed using a second set of one or more convolutional neural networks to determine a retinopathy grade for the at least one fundus image. A risk level of progression of diabetic retinopathy of the individual is determined based on at least the HbA1c level and the retinopathy grade.

Abstract: Data representative of the eye obtained from multiple modalities are combined into a virtual model of the eye. The multiple modalities indicate anatomical, physiological, and/or functional features of the eye. The data from different modalities is registered in order to combine the data into the virtual model. Eye data can be analyzed in the form of the virtual model. Neural networks can be used to obtain insights about medical conditions of the eye, such as for diagnosis or prognosis of conditions. It can also be predicted how the eye will respond to certain treatments.

Abstract: Parameters of an eye are measured by capturing images of the eye when at least one light source is shone into the eye and analyzing the captured images. An ocular biometry system includes a light source configured to generate a light beam, cameras configured to capture images of the eye when the light beam passes through the eye, and processors configured to identify features in the captured images. The features represent the light beam passing from one part of the eye to another part of the eye. One or more parameters of the eye are determined from the identified features. The light beam can be adjusted to be incident on the eye in a number of positions and multiple light beams can be used.