Systems and methods for improving the reliability of glaucoma diagnosis and progression analysis are described. The measurements made from one type of diagnostic device are adjusted based on another measurement using a priori knowledge of the relationship between the two measurements including the relationship between structure and function, knowledge of disease progression, and knowledge of instrument performance at specific locations in the eye. The adjusted or fused measurement values can be displayed to the clinician, compared to normative data, or used as input in a machine learning classifier to enhance the diagnostic and progression analysis of the disease.
