@article{Chandrinos2020b,

title = {A Review of Learning Effect in Perimetry},

author = {A. Chandrinos and D.D. Tzamouranis},

url = {https://www.researchgate.net/publication/340762287_A_Review_of_Learning_Effect_in_Perimetry},

doi = {10.13140/RG.2.2.24127.87203},

year  = {2020},

date = {2020-04-14},

journal = {Ophthalmology Research: An International Journal},

volume = {12},

pages = {23-30},

abstract = {Glaucoma is the second most common cause of visual impairment in the UK, with visual impairment registrations have increased by 22% since 2010. Glaucoma refers to a group of optic neuropathies leading to visual impairment and blindness. If glaucoma remains untreated, it may produce optic nerve damage, leading to vision loss. Consequently, visual field tests can be extremely valuable for glaucoma. At the same time, visual field assessment should be performed at baseline and periodically in the glaucoma follow-up or monitor the effectiveness of adopted therapeutic schemes. Any visual field test can be masked by one or more artefacts, which can either lead to the incorrect result of visual field loss or to the possible deterioration of existing loss. One of the most important factors is the perimetric learning effect that is present in almost all types of perimetry. To minimize the learning effect, we either have to conduct a practice test procedure, as a demonstration for the patient without collecting data, or to calculate and establish a learning index of the specific patient. By the establishment of such an index, assist the clinician in detecting possible masked or overestimated visual field defects or progression of glaucoma damage. Potentially, the intense data collection at a large number of locations throughout the field in a larger cohort of subjects (visually healthy and glaucomatous) would be required for a better index establishment. The incorporation of fatigue also may be required to form a robust index enough to simulate procedures of glaucoma prognosis. The low signal to noise ratio associated with perimetric testing suggests that improvements will always be difficult to make.},

keywords = {filters, learning effect, learning index, perimetry, visual fields loss},

pubstate = {published},

tppubtype = {article}

}