This randomized trial evaluates a virtual multi-eccentric system measuring ocular aberrations, suggesting improved efficiency in ocular assessments.
We present a computational proof of concept, inspired by a recent patent developed by Nikolai Suchkov and Siegfried Wahl (European Patent Office EP4179957A1), of a virtual multi-eccentric Hartmann–Shack (HS) aberrometer for simultaneous measurement of peripheral ocular aberrations. The simulated system records nine wavefronts distributed within a region of approximately 20° around the fovea and is evaluated under ideal and realistic conditions, including speckle and illumination inhomogeneities in HS sensor images. Aberrations are quantified by two approaches: a traditional centroid-based method and a custom-made ResNet convolutional neural network (CNN) trained on simulated HS images. Results show that speckle markedly degrades traditional reconstructions for eccentric points, whereas the CNN (trained with speckled data) exhibits greater robustness and accuracy across all nine wavefronts. This model serves as a proof of concept toward a physical multi-eccentric aberrometer that acquires nine wavefronts in a single shot, thereby eliminating sequential point-by-point acquisition, reducing measurement time, and limiting motion-induced variability between measurements. The approach has potential applications in evaluating ocular treatments and in longitudinal studies of ametropias where peripheral aberrations are implicated.
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MARIN et al. (2026) studied this question.
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