Tracking the Reflective Light Particles Spreading on the Cornea: An Emerging Assessment for Tear Film Homeostasis

Abstract

Purpose To implement an emerging noninvasive approach for assessing the dynamic tear film (TF) homeostasis. Methods The video records of dynamic TF from 12 healthy orthokeratology lens wearers were obtained by a clinically available TF analyzer and decomposed as image sequences. The trajectories of TF particles were analyzed by two tracking models, the full-span model (FSM) and the fixed-duration model (FDM). FSM tracked a particle for a complete opening blink cycle, while FDM tracked 1 second of the same cycle. A power-law fitting operation \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}{\rm{MMS}}\left( t \right) = {\rm{\alpha }} \times {t^{ - {\rm{\beta }}}}\end{document} was used to extract homeostasis markers based on the tracking model for each subject. Results Comparing two tracking models ( N = 6), only one subject had statistical difference in averaged momentary moving speed (MMS; P = 0.0488), while none had significant difference in averaged momentary moving direction (MMD). However, both models showed good correlations in average MMS (ρ = 0.94, P = 0.0048) and MMD (ρ = 1.00, P < 0.0001) and all extracted homeostasis markers [α, β, MMS(0.1), and MMS(2.0)]. Assessing interblink reliability in these markers under FDM tracking ( N = 12), only one subject in the MMS (0.1) and another subject in the MMS (2.0) were outside 95% limits of agreement, respectively. Conclusions FDM is a good alternative to FSM and has tracking properties of higher efficiency and easier implementation. The homeostasis markers under FDM tracking showed a good interblink consistence; therefore this approach will be a promising method for analyzing dynamic TF homeostasis in future practice. Translational Relevance FDM analytical architecture can practice the past experimental platform on a TF analyzer to obtain homeostas...

Comparing Tear Film Viscosity between Sjögren and Non-Sjögren Dry Eye Disease

“…The masked technicians interpreted the examination procedure for the subject before each tear film test. After completing a dry eye questionnaire with Ocular Surface Disease Index (OSDI) [24], each subject received a series of ocular surface examinations with the K5M, including TMH [25][26][27][28][29], TFV [15,16,30,31], ocular surface redness [26,28,29,32], NIKBUT [25][26][27][28][29], and meibography [27,28,33]. Finally, each subject received cobalt blue light illuminated corneal photography after fluorescence dye staining to determine the corneal epitheliopathy [28,29,34].…”

“…Varikooty et al pointed out that tear film spreading could indirectly reflect TFV [14]. Accordingly, the spreading speed of the tear film on the ocular surface can estimate the TFV, in which slower tear film spreading indicates higher friction or external viscosity between tears and the ocular surface [15,16]. Previously, we had established a method to quantify tear film spreading based on a popularly commercialized tear film analyzer, Keratograph ® 5M (K5M; Oculus, GmbH, Wetzlar, Germany) [16].…”

“…Accordingly, the spreading speed of the tear film on the ocular surface can estimate the TFV, in which slower tear film spreading indicates higher friction or external viscosity between tears and the ocular surface [15,16]. Previously, we had established a method to quantify tear film spreading based on a popularly commercialized tear film analyzer, Keratograph ® 5M (K5M; Oculus, GmbH, Wetzlar, Germany) [16]. Briefly, we standardized the positional coordinate of each tear film particle on each K5M videodecomposed image frame with a time interval of 0.1 s. Reflective light particles on the cornea starting from the opening cycle of the blink for 1 s were tracked.…”

“…Briefly, we standardized the positional coordinate of each tear film particle on each K5M videodecomposed image frame with a time interval of 0.1 s. Reflective light particles on the cornea starting from the opening cycle of the blink for 1 s were tracked. The momentary moving speed (MMS) of a tear film particle at a specific moment was defined as the positional change from a frame to the next frame with a time interval of 0.1 s, and the power-law fitting for MMS data was used to extract two estimators (α and β) by MMS (t) = α × t −β , where α is the scaling factor, β is the power factor, and t is the time [16]. The parameters MMS, α, and β were used for TFV analysis.…”

“…The evaluation of TFV based on tear film spreading on the cornea is a noninvasive and emerging method for the dynamic assessment of tear film homeostasis [16]. We hypothesized that the TFV of SS-DED is higher than that of non-SS-DED.…”