Methods and systems in ophthalmic imaging are presented that increase the sensitivity of automated diagnoses by the use of a combination of both functional and structural information derived from a variety of ophthalmic imaging modalities. An example method to analyze image data of an eye of a patient includes processing a first image dataset to obtain one or more functional metrics processing a second image dataset to obtain one or more structural metrics comparing the one or more structural metrics to the one or more functional metrics and processing the results of said comparison to derive the probability of a disease or normality of the eye.
