July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Predicting the positivity for thioflavin fluorescence of retinal deposits by their polarization properties in association with Alzheimer's disease
Author Affiliations & Notes
  • yunyi Qiu
    Physics, University of Waterloo, Waterloo, Ontario, Canada
  • Melanie C W Campbell
    Physics, University of Waterloo, Waterloo, Ontario, Canada
  • Tao Jin
    Physics, University of Waterloo, Waterloo, Ontario, Canada
  • Erik Mason
    Physics, University of Waterloo, Waterloo, Ontario, Canada
  • Rachel Redekop
    Physics, University of Waterloo, Waterloo, Ontario, Canada
  • Laura Emptage
    Physics, University of Waterloo, Waterloo, Ontario, Canada
  • Monika Kitor
    Physics, University of Waterloo, Waterloo, Ontario, Canada
  • Footnotes
    Commercial Relationships   yunyi Qiu, None; Melanie Campbell, LumeNeuro (I), University of Waterloo (P); Tao Jin, None; Erik Mason, None; Rachel Redekop, None; Laura Emptage, None; Monika Kitor, None
  • Footnotes
    Support  NSERC
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1473. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      yunyi Qiu, Melanie C W Campbell, Tao Jin, Erik Mason, Rachel Redekop, Laura Emptage, Monika Kitor; Predicting the positivity for thioflavin fluorescence of retinal deposits by their polarization properties in association with Alzheimer's disease. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1473.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Alzheimer’s disease (AD) is characterized by the deposition of amyloid protein in the brain and neural retina. Here, we present several machine learning methods with the aim of predicting when retinal deposits found in association with the disease are positive in thioflavin fluorescence, based on their polarization properties.

Methods : Eyes (N=31) were obtained from donors with a primary or secondary diagnosis of AD. The retinas were stained with 0.1% Thioflavin-S and counterstained with DAPI. Flat-mounted retinas were scanned under crossed polarized light and each deposit was then imaged with fluorescence and Mueller matrix polarimetry microscopy. Imaged deposits were then categorized by the presence or absence of a Thioflavin signal. For each imaged deposit, 6 polarization properties were examined and compared between deposits with and without fluorescence signal. The data was split into two sets for training and testing. Three algorithms, linear discriminant analysis (LDA), supporting vector machine (SVM) and random forest (RF) were applied to train the classifiers to automatically determine the existence of a fluorescence signal. The accuracy, sensitivity and specificity of each classifier were computed to evaluate the performance of classification. The accuracy was obtained from 10-fold cross-validation; sensitivity and specificity were calculated from the confusion matrix of the test set.

Results : The RF classifier had the best results for the presence or absence of a fluorescence signal, with an accuracy of 94.2%, a sensitivity of 96.7% and a specificity of 96.2%. The accuracy, sensitivity and specificity of the SVM classifier were 93.9%, 95.9% and 95.4%, respectively. Finally, the LDA classifier had an accuracy of 86.7%, a sensitivity of 84.5% and a specificity of 91.1%, which is not as powerful as the first two.

Conclusions : The existence of Thioflavin positivity in retinal amyloid deposits can be predicted from polarization properties imaged without the use of a dye. The machine learning classifiers, random forest (RF) and supporting vector machine (SVM), gave high accuracy. Thus, polarimetry imaging can accurately identify thioflavin positive deposits in the retina. This method in turn could be used for the diagnosis of AD.

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

×
×

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.

×