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P. Limoli, L. D'Amato, R. Solari, A. Ribecca, P. Costanzo, E.M. Vingolo; Virtual Low Vision Patient Correlation Between Virtual and Real Data in Patient's Rehabilitation . Invest. Ophthalmol. Vis. Sci. 2003;44(13):4986.
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© ARVO (1962-2015); The Authors (2016-present)
Pourposes: Visual rehabilitation for low vision patient presents one main problem: it is a lengthy process to test the magnyfying system to ascertain which is the best solution. These lengthy tests reduce the patient's attention, quality of performance, precision of the choice, and finally increase frustration of the patient. Our goal is to find a method to make the visual rehabilitation more simple, quick and precise. Patients and methods: We have created Virtual IPO®, software with a computerzed simulation of patient's visual performances: Virtual IPO® allows the comprehension of visual conditions and automatically suggests the best magnyfication and decentralization necessary to restore reading ability. We have tested virtual visual rehabilitation on 103 patients aged between 18 e 95 years (average 67 years), 62 (60,19%) female and 41 (39,81%) male, considered low vision patients because of their reading disability (residual near visual acuity lower than 10 pts): average BCVA is 0,172 (max 0,9, min. 0,005); average near visual acuity is 39,14 pts with phisiological correction (max 6 pts,m min. 140 pts). Virtual data were used for the visual rehabilitation. We have considered the real and the virtual magnyfication, the reading field (virtual number of letters for fixation), final reading speed (words per minute), the final reading coefficient (words per minute * C-RBT/100, where C-RBT is the comprehension-retention in a short time). Statistical analysis was done by correlation tests on Statwiew program for Apple Macintosh computers. Results: The correlation between virtual and real magnyfication was highly statistically significant (r=0,98 con p=00000,1. Virtual magnyfication was correlated statistically with final reading speed (r= -0,53 con r=0,00001), final reading coefficient (r= -0,53 con r=0,00001), reading field (r= -0,48 con p=0,00001). Conclusions: Virtual representation obtained with Virtual IPO® gives us a virtual patient similar to the real one: we can quickly and precisely test the best magnyfication and decentralization. Necessary magnyfication is inversely proportional to reading field, reading speed, reading coefficient. With Virtual IPO® we must find a way to read with the lower magnyfication and the higher reading field, to obtain the higher final reading coefficient. Moreover, less tests seem to be the best way to increase patient's attention, quality of performance, precision of the choise, and finally less frustration of the patient.
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