The contribution of different physical effects to tear breakup (TBU) in subjects with no self-reported history of dry eye are quantified. An automated system using a convolutional neural network is deployed on fluorescence (FL) imaging videos to identify multiple likely 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. The mathematical models consisted of systems of ordinary differential equations for the aqueous layer thickness, osmolarity, and the FL concentration. 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. Fits were produced for 467 instances of potential TBU from 15 non-DED subjects. The results showed a distribution of causes of TBU in these healthy 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 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.

翻译：针对无自报干眼病史受试者中不同物理效应对泪膜破裂（TBU）的贡献进行量化。采用卷积神经网络自动化系统处理荧光素钠（FL）成像视频，识别每轮试验中多个可能的TBU实例。提取FL强度数据后，通过包含眼球切向流、蒸发、渗透及泪膜发射FL强度的数学模型进行拟合。数学模型由描述水层厚度、渗透压及FL浓度的常微分方程组构成。通过优化模型与FL强度数据的拟合，确定各TBU实例的驱动机制，并估算TBU内部的渗透压值。对15名非干眼病（DED）受试者的467个潜在TBU实例完成拟合。结果显示，这些健康受试者中TBU的原因存在分布差异（如估算的流速与蒸发速率所反映），与已发表数据吻合良好。最终渗透压强烈依赖于TBU机制，通常随蒸发速率增加而升高，但受流速依赖关系的复杂影响。结果表明，该方法可能实现对个体受试者的分类，并为干眼症患者的比较及潜在分类提供基线。