Mathematical modeling highlights from ARVO 2018

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Maki KL, Repetto R, Braun RJ. Mathematical modeling highlights from ARVO 2018. MAIO [Internet]. 2019 Jun. 19 [cited 2024 May 22];2(3):5-8. Available from:

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anterior chamber; dry eye; modeling; posterior chamber; retina; tear film


At the ARVO annual meeting, there is an increasing number of contributions that involve significant mathematical modeling of ocular physiology and procedures. There has long been significant use of statistical methods for understanding data from a variety of in vivo measurements and clinical trials. Beyond these important uses of statistical and mathematical tools, a growing number of researchers are developing mathematical and computational models, often based on fundamental principles from physics, chemistry and mechanics, that provide insights into ocular phenomena. A number of areas had noticeable contributions involving applications of models, such as tear production, tear film dynamics, corneal biomechanics, retinal blood flow, and glaucoma. We list a number of such contributions in this introduction and follow those with five extended abstracts that summarize some of the studies mentioned here.


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Yabusaki K, Arita R, Yamauchi T. Automated classification of dry eye type analyzing interfering fringe color images of tear film using machine learning technologies. Invest Ophthalmol Vis Sci. 2018;59(9):4860.

Cwiklik L, Riedlova K, Melcrova A, Olzynska A, Daull P, Garrigue J. Influence of benzylalkonium chloride on tear film lipid layer stability: a molecular level view by employing in silico modeling. Invest Ophthalmol Vis Sci. 2018;59(9):3279.

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