Auckland Uniservices Limited

Toku Eyes Limited

Seyed Ehsan Vaghefi Rezaei

Li Xie

David Michael Squirrell

Songyang An

Song Yang

A method for prognosing disease in an eye, the method including: receiving present eye image data representative of the eye at a present time; analyzing the present eye image data using a neural network to generate a prediction for future eye image data representative of the eye at a future time; and generating a prognostic parameter for the eye from the future eye image data, wherein the neural network is trained using past eye image data representative of a plurality of eyes at first and second past times to generate one or more eye image data change functions, wherein the eye image data change function is applied by the neural network to the present eye image data to generate the prediction for the future eye image data.

Methods and systems for ocular imaging, diagnosis and prognosis

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.

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.

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. An individual feature vector is produced based on meta-information for the individual, and the outputs of the RCF CNNs. The individual feature vector is processed using a CVD risk prediction neural network model to output a prediction of overall CVD risk for the individual. The model is configured to determine a relative contribution of each of the risk contributing factors to the prediction of overall CVD risk. The overall CVD risk is reported, together with the relative contribution of each of the risk contributing factors to the overall CVD risk.

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Auckland, New Zealand

Seyed Ehsan Vaghefi Rezaei