June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Improved prediction of incident primary open angle glaucoma using automated analysis of optic nerve head structure
Author Affiliations & Notes
  • Mark Christopher
    University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • Michael David Abramoff
    University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • Li Tang
    University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • John H Fingert
    University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • Todd E Scheetz
    University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • Footnotes
    Commercial Relationships Mark Christopher, None; Michael Abramoff, None; Li Tang, None; John Fingert, None; Todd Scheetz, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 1022. doi:https://doi.org/
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Mark Christopher, Michael David Abramoff, Li Tang, John H Fingert, Todd E Scheetz; Improved prediction of incident primary open angle glaucoma using automated analysis of optic nerve head structure. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):1022. doi: https://doi.org/.

      Download citation file:


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

      ×
  • Supplements
Abstract
 
Purpose
 

To computationally identify quantitative features of optic nerve head (ONH) structure and apply those features to model the contribution of ONH structure to glaucoma.

 
Methods
 

ONH structural features were identified using baseline measurements and stereo fundus photos collected from the participants of the Ocular Hypertension Treatment Study. The collected measurements included demographic markers (age, sex, ethnicity), clinical measurements (visual acuity, intraocular pressure, cup-to-disc ratio, central corneal thickness, visual field measurements), and outcomes (conversion to POAG). ONH structure was inferred from the stereo photos using our validated stereo correspondence algorithm. The primary modes of variation of ONH structure were identified using principal component analysis. Contributions of baseline measurements and POAG to ONH structure were modeled using linear discrimant analysis. This resulted in a set of ONH structural features known as structural endophenotypes (STEPs). Association of the STEPs were evaluated against demographic, clinical, and outcome markers. The ability of the STEPs to predict incident POAG was also assessed.

 
Results
 

The computationally-identified STEPs showed significant associations with several clinical markers (age, ethnicity, cup-to-disc ratio, central corneal thickness, visual acuity). Incorporating the STEPs into models of incident POAG prediction led to an AUC of 0.722, a significant increase over baseline models using only demographic markers (AUC = 0.599).

 
Conclusions
 

A novel computational method for analyzing ONH structure was developed and evaluated. The resulting objective, quantitative measurements of ONH structure are significantly associated with widely-used clinical markers and useful in prediction of incident POAG. Future work will incorporate available longitudinal data to determine the stability of STEPs and attempt to identify time-dependent trends that aid in detecting disease onset prior to any loss of vision.  

 
(A). Input stereo fundus images. (B) Isolated ONH region. (C) Inferred 3D ONH information, shown in raw and processed forms. (D) Illustrations of the extracted ONH structure.
 
(A). Input stereo fundus images. (B) Isolated ONH region. (C) Inferred 3D ONH information, shown in raw and processed forms. (D) Illustrations of the extracted ONH structure.
 
 
(A) Representations of several STEPs. (B) Illustrations of changes in ONH structure associated with each STEP. Rows show a median (left) and maximum (right) contribution of the STEP. (C) The same images colored to help changes in structure.
 
(A) Representations of several STEPs. (B) Illustrations of changes in ONH structure associated with each STEP. Rows show a median (left) and maximum (right) contribution of the STEP. (C) The same images colored to help changes in structure.

 
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×