July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Prediction of disease conversion in intermediate AMD from longitudinal OCT using self-supervised deep learning
Author Affiliations & Notes
  • Antoine Rivail
    Ophthalmology, Medical University Of Vienna, Wien, Austria
  • Wolf-Dieter Vogl
    Ophthalmology, Medical University Of Vienna, Wien, Austria
  • Ferdinand Georg Schlanitz
    Ophthalmology, Medical University Of Vienna, Wien, Austria
  • Magdalena Baratsits
    Ophthalmology, Medical University Of Vienna, Wien, Austria
  • Ursula Schmidt-Erfurth
    Ophthalmology, Medical University Of Vienna, Wien, Austria
  • Hrvoje Bogunovic
    Ophthalmology, Medical University Of Vienna, Wien, Austria
  • Footnotes
    Commercial Relationships   Antoine Rivail, None; Wolf-Dieter Vogl, None; Ferdinand Schlanitz, None; Magdalena Baratsits, None; Ursula Schmidt-Erfurth, Böhringer Ingelheim (C), Genentech (C), Novartis (C), Roche (C); Hrvoje Bogunovic, None
  • Footnotes
    Support  Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1355. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Antoine Rivail, Wolf-Dieter Vogl, Ferdinand Georg Schlanitz, Magdalena Baratsits, Ursula Schmidt-Erfurth, Hrvoje Bogunovic; Prediction of disease conversion in intermediate AMD from longitudinal OCT using self-supervised deep learning. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1355.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : The risk of progression to late age-related macular degeneration (AMD) is heterogeneous and difficult to estimate. Current image-based prediction models use specific AMD biomarkers such as drusen, but do not consider the entire morphologic spectrum nor the speed of morphologic change. We aim at predicting individual conversion in eyes with intermediate AMD using longitudinal OCT imaging representation beyond conventional biomarkers.

Methods : To encode longitudinal OCT, a deep neural network was trained in a self-supervised way by predicting the appearance of an unseen follow-up B-scan from a sequence of preceding observations (Fig .1). The trained network provides a generic representation that simultaneously encodes the structure of the retina and its evolution over time. By combining the representations of all B-scans, we obtained a representation of an OCT volume at that time-point. The compact representation was used as input to a machine learning classifier to predict whether an individual eye will convert to GA or CNV.

Results : The methodology was trained and evaluated on a longitudinally registered dataset of 57 patients with bilateral intermediate AMD (98 eyes, 424 OCTs, followed over three months intervals for a duration of 3-7 years), with 9 patients converting to GA and 10 to CNV during the study. The classification tasks consisted of predicting whether the eye was going to show evidence of conversion three months after the last visit. Using the novel representation the classification resulted in a ROC AuC of 0.90 (specificity of 0.85 at 0.80 sensitivity) for GA and a ROC AuC of 0.65 for CNV (specificity of 0.49 at 0.80 sensitivity).

Conclusions : Our proposed method effectively utilizes longitudinal imaging by learning a generic compact representation of the aging retina and its evolution over time. Our representations shows promising results in predicting incoming conversion to late AMD and may improve personalized risk assessment in the management of AMD. Similar to previous studies, the onset of GA can be more reliably predicted than imminent CNV. The unbiased unsupervised approach offers the potential to identify previously unknown features in AMD, an entity with largely undisclosed pathogenesis.

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.

×