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Rayanne A Luke, Richard J Braun, Tobin Driscoll, Dominick Sinopoli, Vishruta Yawatkar, Luyang You, Aashish Phatak, Carolyn Begley; Fitting Simplified Models to Machine Learning-Identified Tear Film Breakup. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1315.
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© ARVO (1962-2015); The Authors (2016-present)
Six ordinary differential equation (ODE) tear film breakup (TBU) models are created to capture evaporation, osmosis, and different types of flow. A convolutional neural network designed after Su et al. (IEEE, 2018) and trained on over 40,000 image patches successfully identifies TBU and non-TBU in fluorescent (FL) images gathered in vivo with an accuracy of 92%. The ODE models are fit to the automatically identified TBU FL intensity data by optimizing TBU quantities such as evaporation and tangential flow rates. Best-fit determination of TBU parameters suggests which mechanisms cause the thinning.
The FL intensity data was originally recorded from 25 normal subjects with 20 trials taken over two visits (Awisi-Gyau, Indiana University thesis, 2020). We extract the experimental FL image data from the centers of TBU regions identified by a convolutional neural network. These are fit with the models designed to mimic evaporation-driven, tangential flow-driven, and combination thinning. We estimate parameters using a least squares minimization of the difference between experimental and computed intensities using the trust region-reflective method. Theoretical intensity dependent on tear film (TF) thickness and FL concentration was based on Nichols et al (2012, IOVS, 53:5426). Separate procedures were used to estimate initial FL concentration and localized TF thickness (Wu et al, IOVS 2015, 56:4211). The fits use up to four parameters: evaporation rate v, a (steady) and b1 (decaying) extensional flow rates, and decay rate b2. All computations are via custom Python programs.
Optimal evaporation rates fall near or within experimental ranges (Nichols et al, IOVS, 2005). An example fit is shown in the Figure. The best-fit model determines that the evaporation rate is -3.25 μm/min and that the instance exhibits strong, outward tangential flow that decays, allowing evaporation to take over in importance.
Intensity decay in automatically identified TBU areas can readily be fit with simplified models that capture essential thinning dynamics and yield physically relevant quantities. This procedure can be applied to a wide range of instances to obtain statistical information that cannot be directly measured during breakup.
This is a 2021 ARVO Annual Meeting abstract.
Left: Automatically identified TBU instances. Right: Best-fit results for the six ODE models for the box 10 TBU (indicated by arrow in left image).
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