Visual field data points merging to form a comprehensive image.

Beyond the Standard Eye Chart: How Spatial Interpolation is Revolutionizing Vision Testing

Samir D’Costa in Health & Wellness November 2025 • 4 min read.

"Discover how a groundbreaking approach to microperimetry is enhancing early detection of visual defects and transforming eye care."

For years, eye exams have relied on standard charts, but what happens when your vision doesn't quite fit the mold? Traditional methods often fall short in providing a comprehensive assessment, especially for individuals with unique visual field patterns. This limitation has spurred innovation in the field of eye care, leading to the development of advanced techniques that offer a more personalized and accurate evaluation.

One such advancement is gaze-contingent microperimetry, a sophisticated method that tracks eye movements to tailor visual field tests to an individual's specific fixation patterns. While microperimetry offers numerous benefits, comparing results to normative data has been challenging due to the variability in test locations between patients.

Now, a groundbreaking study from the University of Nottingham introduces a novel solution: spatial interpolation. This innovative approach enables clinicians to compare visual field sensitivities against normative data, regardless of the test pattern used. By creating detailed surface maps of visual field data, spatial interpolation enhances the precision and clinical utility of microperimetry, paving the way for earlier and more accurate detection of visual defects.

Unlocking Precision: How Spatial Interpolation Works

Visual field data points merging to form a comprehensive image.

Spatial interpolation is a statistical method that estimates values at unknown locations based on the values at known locations. In the context of microperimetry, this means creating a continuous surface representing visual field sensitivity across a wide area, even if data points are not available for every single location. This surface is built from a dense grid of test locations in a group of healthy individuals, allowing for comparisons of sensitivity measurements from varied visual field locations.

The study used data from 60 healthy participants aged 19-50 who underwent microperimetry (MAIA-2) using 237 spatially dense locations. Researchers then fitted surfaces to the mean, variance, and 5th percentile sensitivities to create a normative dataset. A key aspect of the study was assessing the goodness-of-fit of these surfaces. This involved refitting the surfaces multiple times and comparing estimated sensitivities with measured sensitivities at randomly excluded locations.

Universal Kriging: A spatial interpolation technique was used for the modelling, and it was identified as one of the best methods.
Goodness-of-fit Assessment: Root mean square (RMS) differences between estimated and measured sensitivities were calculated to assess the accuracy of the interpolation.
Leave-One-Out Method: Individual data were compared with the 5th percentile surface to evaluate how well the interpolation identified sensitivities falling below the expected range.
Positional Error Simulation: Error was added to the test grid to mimic cases with unknown fovea location, and the impact on the number of locations falling beneath the 5th percentile was assessed.

The results demonstrated high accuracy, with RMS differences between estimated and measured sensitivities remaining low. Moreover, the number of sensitivities falling beneath the 5th percentile surface closely matched the expected 5%, indicating the reliability of the spatial interpolation method. The positional error simulation further confirmed the robustness of the approach, with minimal impact on the overall results.

The Future of Vision Testing: Enhanced Clinical Utility

The implications of this study are far-reaching. By enabling comparison of sensitivity measurements from varied visual field locations, spatial interpolation unlocks new possibilities for clinical practice. Clinicians can now generate familiar indices and probability maps, similar to those used in standard automated perimetry, but tailored to the unique fixation patterns of each patient. This advancement holds particular promise for individuals with nonfoveal fixation or central vision loss, offering a more accurate and personalized assessment of their visual function. The use of microperimetry can be enhanced with special data comparison methods, especially in cases of nonfoveal fixation.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1167/iovs.16-20222, Alternate LINK

Title: Spatial Interpolation Enables Normative Data Comparison In Gaze-Contingent Microperimetry

Subject: General Medicine

Journal: Investigative Opthalmology & Visual Science

Publisher: Association for Research in Vision and Ophthalmology (ARVO)

Authors: Jonathan Denniss, Andrew T. Astle

Published: 2016-10-19

Everything You Need To Know

What is spatial interpolation and how is it used in vision testing?

Spatial interpolation is a statistical method that estimates values at unknown locations based on values at known locations. In the context of vision testing, specifically in microperimetry, it creates a continuous surface representing visual field sensitivity. This method takes data from a dense grid of test locations to compare sensitivity measurements from varied visual field locations, even if data points are not available for every single location. This method is crucial for analyzing visual field data with precision and detecting visual defects early. This helps improve accuracy for individuals with unique visual field patterns.

How does gaze-contingent microperimetry improve vision testing compared to traditional eye charts?

Gaze-contingent microperimetry is a sophisticated method that tracks eye movements to tailor visual field tests to an individual's specific fixation patterns, unlike standard eye charts. Traditional charts may not provide a comprehensive assessment for individuals with unique visual field patterns. By adapting to each patient's gaze, microperimetry offers a more personalized and accurate evaluation, especially beneficial for those with non-standard vision issues. This technique significantly improves the accuracy and depth of visual field testing, resulting in early detection of visual defects.

What were the key methods used in the study to assess the effectiveness of spatial interpolation?

The study employed several key methods to assess spatial interpolation's effectiveness. These include the application of Universal Kriging as a spatial interpolation technique, which was found to be optimal for modeling. A goodness-of-fit assessment calculated root mean square (RMS) differences between estimated and measured sensitivities to evaluate accuracy. The study also used a Leave-One-Out Method comparing individual data with the 5th percentile surface to assess sensitivity accuracy. Positional error simulation was used to check the robustness of the approach under varied fovea locations, by adding error to the test grid and assessing the effect on the number of locations falling below the 5th percentile.

What are the advantages of using spatial interpolation with microperimetry for patients with nonfoveal fixation?

For patients with nonfoveal fixation or central vision loss, spatial interpolation offers significant advantages. It enables clinicians to generate familiar indices and probability maps, similar to standard automated perimetry but tailored to the patient's unique fixation patterns. This approach allows for a more accurate and personalized assessment of visual function, a critical improvement over traditional methods that may not account for these specific vision challenges. By providing a more detailed and customized evaluation, spatial interpolation enhances the clinical utility of microperimetry, particularly in these cases.

How does the use of Universal Kriging, and RMS differences help improve vision testing outcomes?

Universal Kriging is a specific spatial interpolation technique identified as one of the best methods for modeling visual field data. This technique is applied to create detailed surface maps from the data points. Root mean square (RMS) differences, which measure the accuracy of the interpolation, are used to compare estimated and measured sensitivities. Low RMS differences indicate high accuracy, meaning the interpolation method effectively estimates visual field sensitivities at various locations. This ensures the reliability of the results, enabling precise identification of visual defects. These combined methods increase the overall accuracy and reliability of vision testing and enhance its clinical utility.