June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Mobile application software (App) for Prediction of Advanced Age-related Macular Degeneration
Author Affiliations & Notes
  • Min-Lee Chang
    USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
  • Yu-Hsuan Chiu
    Concord-Carlisle High School, Concord, MA
  • Chung-Jung Chiu
    USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
    Department of Ophthalmology School of Medicine, Tufts University, Boston, MA
  • Luca Avoni
    Department of Ophthalmology, Bologna Hospital Ospedale Maggiore, Bologna, Italy
  • Footnotes
    Commercial Relationships Min-Lee Chang, None; Yu-Hsuan Chiu, None; Chung-Jung Chiu, None; Luca Avoni, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 4111. doi:
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      Min-Lee Chang, Yu-Hsuan Chiu, Chung-Jung Chiu, Luca Avoni; Mobile application software (App) for Prediction of Advanced Age-related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):4111.

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

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Abstract
 
Purpose
 

To develop a state-of-the-art mobile application software (App) for the macular risk scoring system (MRSS).

 
Methods
 

The MRSS algorithm uses information from a patient’s age, sex, race, education level, smoking status, macular pigmentation (normal vs. abnormal), maximal drusen size, and drusen texture (hard vs. soft) to calculate a risk score for progression to advance age-related macular degeneration (AMD). Based on a one-page prototype of the MRSS App for the Age-Related Eye Disease Study (AREDS) that have been developed and published along with the MRSS paper (Chiu et al., Ophthalmology 2014;121:1421-1427.), we wrote a user-friendly App for iOS devices (iPhones/iPads) and Android devices and submitted the App to the Apple App Store and the Google Play, respectively.

 
Results
 

The App consists of 12 pages, including a cover page, a welcome page, a credit page, 8 question pages for the eight predictors, and a result page that automatically presents the result from the MRSS. Before submitted for download, the App was tested to ensure consistent results with both of the prototype of the App and manual calculation from the MRSS algorithm. The App is free for download at https://itunes.apple.com/us/app/macular-degeneration-test/id944048670?l=it&ls=1&mt=8 for iOS devices and at https://play.google.com/store/apps/details?id=com.arscolor.app.mrss&hl=it for Android devices.

 
Conclusions
 

We have refined the AREDS MRSS App prototype and made the App available for download via Internet. To our knowledge, our App is the first mobile application for the prediction of advanced AMD. Based on the results from the MRSS paper, this App can serve as a platform for modifications to work in other populations.  

 
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