Application of Computational Tear Flow Models to Smart Contact Lens Design

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Millions of diabetics could benefit from a noninvasive and cheaper method for monitoring their blood glucose. A solution recently popularized by Google makes use of smart contact lenses featuring embedded glucose sensors which can detect and wirelessly transmit their measurements. This technology takes advantage of the glucose present in the aqueous humor of the human eye, which is proportional to the glucose present in the blood. The technology to produce such contact lenses is well established, but few numerical models are available to characterize the system. In particular the tear fluid dynamics responsible for distributing the glucose on the eye surface is not well understood. A model describing this behavior would facilitate the development, optimization, and prototyping of a smart contact lens that diabetic patients can depend on. The goal of this project was to take advantage of 3D computational modeling to optimize glucose sensor placement on a contact lens. Several partial models were implemented to capture aspects of tear flow. An initial computational model was implemented based on a physical prototype [1]. It featured two inlets and one outlet, but did not provide a fully representative model with respect to physiological fluid flow on the eye. However, the experimental values from that project were sufficient to validate the physical accuracy of the computational model. Once this was established, a second model was implemented to take into account tear flow from the lacrimal gland across the eye to the lacrimal ducts. The locations of the inflow and outflow were selected to match physiological eye models [8]. A third model configuration simulates gravitational tear flow from the top eyelid to the bottom [7]. This was implemented as a constant inflow from the upper edge of the lens, and an outflow from the bottom edge. These three different models each captured a single aspect of physiological tear flow, so each predicted different profiles of fluid flow and glucose homogenization times. A combined model was created to weigh all these aspects of tear flow. This combined model was used to optimize locations for a glucose sensor based on glucose equilibration times at different locations within the model. The models were demonstrated to be physically consistent and to be insensitive to the variable physiological parameters of tear flow velocity and glucose diffusivity, as well as to the computational parameter of mesh resolution. Subsequent experiments in the combined model yielded an optimized location for the glucose sensor that fit all the design criteria: avoiding occlusion of vision, providing adequate space for the sensor, and demonstrating fast equilibration time. A new sensor placement was proposed for subsequent design iterations of the lens. This location is closer to the upper eyelid than in the initial physical model. This optimized position decreased concentration equilibration time by 30%. These results demonstrate the utility of computational models in the design of smart contact lenses. In particular the implementation of these models can allow very rapid prototyping of design concepts. These models demonstrate the viability of smart contact lenses and their potential as glucose monitoring solutions for diabetic patients, and to become a suitable alternative to lancet-based glucometers.
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Computational Tear Flow Models; Smart Contact Lens Design
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