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.