Toku Eyes Limited

David Michael Squirrell

Song Yang

Songyang An

Li Xie

Seyed Ehsan Vaghefi Rezaei

Systems and methods for determining one or more recommendations for management of wellbeing of an individual are disclosed. An indication of relative cardiovascular aging of the individual is determined based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images. The recommendations for management of the individual's wellbeing are based at least in part on a determined difference between the actual chronological age of the individual and an indication of relative cardiovascular aging of the individual.

Systems and methods for processing of fundus images

Systems and methods for determining one or more recommendations for management of wellbeing of an individual are disclosed. An indication of relative cardiovascular aging of the individual is determined based at least in part on a predicted risk of cardiovascular disease (CVD) of the individual determined by a deep learning model based on one or more fundus images. The recommendations for management of the individual's wellbeing are based at least in part on the determined indication of relative cardiovascular aging.

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

David Michael Squirrell