研究目的
To demonstrate methods that enable visual field sensitivities to be compared with normative data without restriction to a fixed test pattern.
研究成果
Spatial interpolation methods enable accurate comparison of microperimetric sensitivities to normative data at varied locations, facilitating the calculation of familiar clinical indices and probability maps. This approach enhances the utility of microperimetry, particularly in patients with nonfoveal fixation, and supports early detection of visual defects in retinal pathologies like AMD.
研究不足
The normative database is small (n=60) with a narrow age range, collected from a single site, and not definitive for clinical use. Positional uncertainty in cases of unknown fovea location may affect accuracy, though simulations showed minimal impact. Age-related sensitivity adjustments were not fully incorporated due to lack of public data.
1:Experimental Design and Method Selection:
The study used spatial interpolation methods, specifically Universal Kriging with a quadratic trend surface and exponential covariance matrix, to fit surfaces to normative microperimetry data. This was chosen to enable comparison of sensitivity at any spatial location.
2:Sample Selection and Data Sources:
60 healthy participants aged 19-50 were recruited, with inclusion criteria including visual acuity ≤
3:2 logMAR, specific refractive error ranges, and no ocular disease. One eye per participant was tested using the MAIA-2 microperimeter. List of Experimental Equipment and Materials:
MAIA-2 microperimeter (CenterVue), Goldmann III stimuli, R software (version
4:0) with MASS and spatial packages. Experimental Procedures and Operational Workflow:
Participants underwent microperimetry with a custom grid of 237 locations, tested in four blocks. Sensitivity thresholds were estimated using a 4-2 staircase algorithm. Surface fitting involved excluding certain locations, selecting parameters via grid search, and assessing goodness-of-fit through resampling and leave-one-out methods. Positional error simulations were conducted by shifting test grids based on fovea-optic disc distribution.
5:Data Analysis Methods:
Root mean square differences were calculated to assess surface accuracy. Statistical analyses included median and interquartile ranges, and Spearman's correlation for positional effects.
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