Biomedical subjects

Beatriz Antona

Publications and source records attributed to Beatriz Antona.

3 recordsLinked to original sources

PubMed2007

Validity and repeatability of a new test for aniseikonia.

PURPOSE: The Aniseikonia Inspector 1.1 (AI) is a new software product to measure aniseikonia using red-green anaglyphs. The purpose of this study was to test whether the AI is a valid and reliable test. METHODS: There were two groups of sample subjects: one at risk of aniseikonia, with anisometropia greater than or equal to 1.00 D (n= 29), and a control group (n= 45). The validity was studied by comparing the measured aniseikonia with the aniseikonia simulated with size lenses. The reliability was estimated by the Bland-Altman statistical method. RESULTS: The results showed that the AI underestimated aniseikonia and that the underestimation was greater in the horizontal than in the vertical direction. The reliability was low, with biases that were clinically insignificant, but the 95% limits of agreement were around +/-2%. The behavior of the test was similar in both groups of subjects. CONCLUSIONS: The reliability of the AI is only moderate, and professionals are therefore warned to use the results of this test with caution.

Adult↗

PubMed2006

The validity and repeatability of the New Aniseikonia Test.

PURPOSE: Aniseikonia has been traditionally measured using the New Aniseikonia Test (NAT) based on red/green anaglyphs. This study was designed to establish whether the NAT is a valid and reliable test. METHODS: The NAT was tested on three groups of subjects: a control group (n = 45) and two groups of participants at risk of experiencing aniseikonia, those with anisometropia greater than or equal to 1.00 D (n = 29) and those with bilateral pseudophakia (n = 26). The validity of the test was established by comparing the measured aniseikonia with that simulated with size lenses in a double-blind study. Repeatability was evaluated by comparing the results obtained at two different time points. RESULTS: Our results indicate that the NAT underestimates aniseikonia and more so in the horizontal than in the vertical direction. Repeatability was poor, although biases were clinically insignificant. However, the 95% limits of agreement were around +/-2%. The behavior of the test was similar in the three groups of subjects. CONCLUSIONS: We conclude that the repeatability of the NAT is not very high and recommend that clinicians be cautious when interpreting the results of this test.

Adult↗

PubMed2003

Bayesian model of Snellen visual acuity.

A Bayesian model of Snellen visual acuity (VA) has been developed that, as far as we know, is the first one that includes the three main stages of VA: (1) optical degradations, (2) neural image representation and contrast thresholding, and (3) character recognition. The retinal image of a Snellen test chart is obtained from experimental wave-aberration data. Then a subband image decomposition with a set of visual channels tuned to different spatial frequencies and orientations is applied to the retinal image, as in standard computational models of early cortical image representation. A neural threshold is applied to the contrast responses to include the effect of the neural contrast sensitivity. The resulting image representation is the base of a Bayesian pattern-recognition method robust to the presence of optical aberrations. The model is applied to images containing sets of letter optotypes at different scales, and the number of correct answers is obtained at each scale; the final output is the decimal Snellen VA. The model has no free parameters to adjust. The main input data are the eye's optical aberrations, and standard values are used for all other parameters, including the Stiles-Crawford effect, visual channels, and neural contrast threshold, when no subject specific values are available. When aberrations are large, Snellen VA involving pattern recognition differs from grating acuity, which is based on a simpler detection (or orientation-discrimination) task and hence is basically unaffected by phase distortions introduced by the optical transfer function. A preliminary test of the model in one subject produced close agreement between actual measurements and predicted VA values. Two examples are also included: (1) application of the method to the prediction of the VAin refractive-surgery patients and (2) simulation of the VA attainable by correcting ocular aberrations.

Bayes Theorem↗