Fitting ODE models of tear film breakup

Several elements are developed to quantitatively determine the contribution of different physical and chemical effects to tear breakup (TBU) in normal subjects. Fluorescence (FL) imaging is employed to visualize the tear film and to determine tear film (TF) thinning and potential TBU. An automated system using a convolutional neural network was trained and deployed to identify multiple TBU instances in each trial. Once identified, extracted FL intensity data was fit by mathematical models that included tangential flow along the eye, evaporation, osmosis and FL intensity of emission from the tear film. Optimizing the fit of the models to the FL intensity data determined the mechanism(s) driving each instance of TBU and produced an estimate of the osmolarity within TBU. Initial estimates for FL concentration and initial TF thickness agree well with prior results. Fits were produced for $N=467$ instances of potential TBU from 15 normal subjects. The results showed a distribution of causes of TBU in these normal subjects, as reflected by estimated flow and evaporation rates, which appear to agree well with previously published data. Final osmolarity depended strongly on the TBU mechanism, generally increasing with evaporation rate but complicated by the dependence on flow. The method has the potential to classify TBU instances based on the mechanism and dynamics and to estimate the final osmolarity at the TBU locus. The results suggest that it might be possible to classify individual subjects and provide a baseline for comparison and potential classification of dry eye disease subjects