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Spectral domain OCT (SDOCT), also known as Fourier domain OCT, is a relatively recent advance in imaging that allows clinicians to detect retinal pathologies that might not be seen by the clinician or by using other ophthalmic instruments.Essentially, SDOCT identifies differences in optical reflection of the different retinal layers and illustrates them in a gray scale.In [24], the authors proposed spatial modeling of visual fields to enhance glaucoma progression detection accuracy.By properly modeling visual field dependencies, the authors reached a reasonable degree of accuracy detecting glaucomatous progression.

Standard automated perimetry (SAP) is a psychophysical test that provides the clinicians an insight to the function of the visual field by reporting the retinal sensitivity to light stimuli [8].

Machine learning classifiers (MLCs) have been widely used in biomedical areas [12]–[14].

In an attempt to further increase the objective interpretation of optical imaging and visual function testing, MLCs that use a mathematical approach without human intervention are used to detect defect patterns and disease progression and to classify eyes as healthy or glaucomatous (see [15] for a review).

Machine learning classifiers were employed to detect glaucomatous progression using longitudinal series of structural data extracted from retinal nerve fiber layer thickness measurements and visual functional data recorded from standard automated perimetry tests.

Using the collected data, a longitudinal feature vector was created for each patient's eye by computing the norm 1 difference vector of the data at the baseline and at each follow-up visit.

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