Systems and methods for processing of fundus images

A method of detecting glycosylated haemoglobin (HbA1c) levels from at least one fundus image, the method performed by one or more processors, the method comprising the following three steps in sequence:

• (a) processing one or more fundus images associated with an individual using a first Quality Assurance (QA) set of one or more convolutional neural networks (CNNs) to determine whether the one or more fundus images are of sufficient quality for further processing;
• (b) processing the one or more fundus images determined to be of sufficient quality for further processing using a second eye-identification set of one or more CNNs (eye-ID CNN), to identify the one or more fundus images belonging to a single eye;
• and
• (c) processing the one or more fundus images belonging to the single eye using a third glycosylated haemoglobin (HbA1c) set of one or more CNNs to determine an HbA1c level for the individual,
• wherein the first QA set of one or more CNNs, the second eye-identification set of one or more CNNs and the third glycosylated haemoglobin (HbA1c) set of one or more CNNs are computer-driven CNNs that have been previously trained over multiple iterations by a plurality of sample images previously labelled and quantified by an expert clinician, and previously validated using a plurality of test images from a plurality of test subjects,
• wherein the at least one fundus image is a two-dimensional visible-light photograph, and
• wherein an accuracy in determining the HbA1c level from the at least one fundus image is improved by using the combination of the three sets of the one or more CNNs compared to a determination made without using the three sets of the one or more CNNs.