%0 Journal Article
%A Lai, Hung-Yin
%A Kuo, Ming-Tse
%A Fang, Po-Chiung
%A Lin, Chi-Chang
%A Chien, Chun-Chih
%A Cho, Wan-Hua
%A Chen, Alexander
%A Lai, Ing-Chou
%T Tracking the Reflective Light Particles Spreading on the Cornea: An Emerging Assessment for Tear Film Homeostasis
%B Translational Vision Science & Technology
%D 2019
%R 10.1167/tvst.8.3.32
%J Translational Vision Science & Technology
%V 8
%N 3
%P 32-32
%@ 2164-2591
%X To implement an emerging noninvasive approach for assessing the dynamic tear film (TF) homeostasis. 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 \(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\({\rm{MMS}}\left( t \right) = {\rm{\alpha }} \times {t^{ - {\rm{\beta }}}}\) was used to extract homeostasis markers based on the tracking model for each subject. 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. 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. FDM analytical architecture can practice the past experimental platform on a TF analyzer to obtain homeostasis markers of TF.
%[ 2/27/2020
%U https://doi.org/10.1167/tvst.8.3.32