Computational study demonstrates rapid tear film thickness and osmolarity estimation using neural operators, indicating scalable analysis for dry eye disease.
Tear film (TF) breakup is a key driver of understanding dry eye disease, and estimating TF thickness and osmolarity from fluorescence (FL) imaging typically requires solving computationally expensive inverse problems. We propose an operator learning framework that replaces traditional inverse solvers with neural operators trained on simulated TF dynamics. This approach offers a scalable path toward rapid, data-driven analysis of tear film dynamics.
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Chen et al. (2026) studied this question.
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