Predicting clinical parameters relating to glaucoma from central visual field patterns

A system comprising:

• a processor;
• an output device;
• and
• a non-transitory computer readable medium storing instructions executable by the processor to provide:
• a preclassifier that classifies the patient into one of a plurality of categories representing the severity of glaucoma for the patient and selects the set of patterns from a plurality of sets of patterns defined via archetypal analysis according to the category into which the patient is classified;
• a pattern decomposition component that receives a set of visual field data for a patient representing, for each of a plurality of locations in the central region of an eye of the patient eye, a deviation in sensitivity to a visual stimulus from an age-adjusted normal value and decomposes the set of visual field data into a linear combination of the selected set of patterns defined via archetypal analysis to provide a set of decomposition coefficients;
• a machine learning model that determines a clinical parameter for the patient from at least the set of decomposition coefficients;
• and
• a user interface that provides the determined clinical parameter to a user at the output device.