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this positive effect seems to be related to better performance due to shorter test times. Nevertheless, a new baseline should be established when patients are switched from the standard threshold automated perimetry algorithm to SITA.

Furthermore, correlation between SITA MD and PSD indices and FT MD and PSD, as well as retinal nerve fiber layer (RNFL) thickness, was demonstrated.

On the other hand, inaccuracy of fixation monitoring and lack of sensitivity in detecting small scotomata still affect SITA SAP.

The impact of converting from Humphrey 24-2 FT VF testing to SS VF testing during the followup phase of a clinical trial has been evaluated by Musch et al. (1999) in the CIGTS. The CIGTS VF score and GHT differed between SS and FT tests. Changing the approach used to measuring a study’s primary VF outcome should be accompanied by a critical evaluation of the change’s impact.

Clusters of related locations of the VF have been used to investigate the structure–function relationship and to aid in the detection of glaucomatous progression by reducing the effect of long-term variability.

Machine learning classifiers (MLC) can learn complex patterns and trends in data; the application of MLC to longitudinal SAP data from ocular hypertensive eyes allowed to predict the confirmed abnormality much earlier (on average, 3.9270.55 years) than traditional STATPAC-like methods.

Future work is needed to investigate the potential for using MLC to optimize VF assessment, in particular if field data are combined with structural imaging data.

SAP: the relationship to other functional and structural diagnostic tests in glaucoma

SAP is not selective for a particular ganglion cell type. Any of the primary ganglion cell subtypes can respond to a static achromatic incremental stimulus presented on an achromatic background. Since there is considerable overlap in the receptive fields of retinal ganglion cells, a test with a nonselective stimulus may not be highly sensitive for the earliest loss of retinal ganglion cells. The

reason relies on the considerable redundancy in the coverage of a given location in the retina. Therefore, SAP may not detect VF loss until the optic nerve has already suffered considerable damage, unless the damage is very localized (Delgado et al., 2002).

On the contrary, Short-Wavelength Automated Perimetry (SWAP), Frequency Doubling Perimetry (FDP), Motion Automated Perimetry (MAP), and High-Pass Resolution Perimetry (HPRP) are considered as selective tests.

SAP, FDP-Matrix

FDP and SAP perform similarly in their ability to detect VF defects in early-to-moderate glaucoma. Larger and deeper defects detected with FDP suggest the possibility of earlier detection at high specificity.

These benefits, however, can only be conclusively demonstrated in prospective longitudinal studies.

In comparison with SAP, the sensitivity and specificity of FDP were 92% and 98% with GHT criteria and 98% and 93% with PSDo5% criteria, respectively.

Similarly, high diagnostic precision was found with MD and PSD (at 95% specificity, MD and PSD sensitivity was 82% and 90%, respectively). However, other authors found the Matrix examination did not detect 36% of abnormal SITA fields. Matrix field defects were smaller and deeper than those appearing in SITA perimetry.

In another study, the FDP-Matrix and SAPSITA detected abnormal visual function in 51% and 44%, respectively, of GAOD eyes, and both perimetry techniques identified 11% of healthy eyes as abnormal. Agreement between FDPMatrix and SAP-SITA was moderate (Artes et al., 2005a, b; Haymes et al., 2005; Pierre-Filho et al., 2006; Ferreras et al., 2007; Leeprechanon et al., 2007; Patel et al., 2007).

SAP, SWAP, HPRP, FDT

In a recent study, SAP performance resulted equal to or slightly better than SWAP and not significantly different from FDT and HPRP. This