July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Automated remote visual field test results visualization
Author Affiliations & Notes
  • Brian Madow
    Ophthalmology, Unifersity of Florida, Jacksonville, Florida, United States
  • Footnotes
    Commercial Relationships   Brian Madow, None
  • Footnotes
    Support  none
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2483. doi:
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      Brian Madow; Automated remote visual field test results visualization. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2483.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose : To create automated, robust and easily customizable imaging software to display the results from previously performed automated visual field tests on multiple machines, remotely in the clinic. Currently limited commercial solutions are available that are often not tailored to the need of each physician, not flexible to upgrade, not easily adaptable to the current network or operating system and not always easily affordable especially for small ophthalmic practices.

Methods : Microsoft foundation classes were used to create the software. It was aimed to run on Windows based computers with operating system Windows 7 and higher. The program was designed to include interface module with the purpose to load the testes in a specially constructed network based secure database. Peripheral viewing client software was developed, which included facile graphical user interface. Additionally backup module was implemented to independently backup the database files daily on a remote computer. The software included option to run on a single or dual monitor setup.

Results : The software was designed to build the database automatically from 3 simultaneously active visual field machines. At each station an interface module was installed in order to capture the test immediately after it was completed. Total of 8451 unique patients were imported in the database. There were total of 38738 separate tests loaded which included different strategy measurement for the visual tests. The reliability of the data import was 100%. It was checked by loading the same data 3 times on separate instances. No duplicates or omissions were detected. The software performed reliably and met the expectations of the glaucoma specialists. They were able to compare tests to each other for both eyes or single eye simultaneously. We found that the time for test display is less than 1 sec when the database is on the same computer or it delayed to 1-3 sec as a function of the network status when the database was in remote network location. The backup software successfully archived the database every night and kept 3 separate copies.

Conclusions : Fast, reliable, secure and customizable software was created to facilitate robust remote visualization of previously performed visual field tests. In addition it has broad capabilities for results queries, data mining and research analysis. It can be used as well in a Reading center environment for the purposes of multicenter clinical trials.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.


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