Investigative Ophthalmology & Visual Science Cover Image for Volume 63, Issue 7
June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Characterising DARC (Detecting Apoptosing Retinal Cells) spots in Optic Neuritis (ON) and healthy eyes
Author Affiliations & Notes
  • Soyoung Choi
    Institute of Ophthalmology, University College London, London, London, United Kingdom
    Novai Ltd., United Kingdom
  • John Maddison
    Novai Ltd., United Kingdom
  • Richard Nicholas
    Division of Brain Sciences, Imperial College London, London, London, United Kingdom
  • M Francesca Cordeiro
    Novai Ltd., United Kingdom
    Glaucoma & Retinal Neurodegnrtn Res Grp, Imperial College, UCL 100, Western Eye Hsp, United Kingdom
  • Footnotes
    Commercial Relationships   Soyoung Choi Novai Ltd., Code E (Employment); John Maddison Novai Ltd., Code E (Employment); Richard Nicholas None; M Francesca Cordeiro Visufarma, Code C (Consultant/Contractor), Allergan, Code C (Consultant/Contractor), Aerie Pharmaceuticals, Code C (Consultant/Contractor), Novai Ltd., Code E (Employment), Heidelberg Engineering, Code F (Financial Support), Thea, Code F (Financial Support), Santen, Code F (Financial Support), Novartis, Code R (Recipient)
  • Footnotes
    Support   Wellcome Trust (grant no. WT099729)
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 1573 – A0362. doi:
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      Soyoung Choi, John Maddison, Richard Nicholas, M Francesca Cordeiro; Characterising DARC (Detecting Apoptosing Retinal Cells) spots in Optic Neuritis (ON) and healthy eyes. Invest. Ophthalmol. Vis. Sci. 2022;63(7):1573 – A0362.

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

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Abstract

Purpose : ON is a common feature of Multiple Sclerosis (MS). Around 25% of MS patients present with ON as their initial symptom and approximately 50% of MS patients experience ON. DARC consists of an intravenously administered fluorescent-tagged annexin V (ANX776) which binds to phosphatidylserine, identifying cell stress and apoptosis, with retinal imaging. Here, we sought to differentiate DARC spots in ON subjects from healthy subjects by observing common descriptive measures (DM) of DARC spots identified by a previously described convolutional neural network (CNN)-aided algorithm.

Methods : DARC images were obtained and anonymised from ON (mean age = 46) and healthy (mean age = 48) participants from the Phase 2 DARC clinical trial (ISRCTN10751859). Recruitment of participants was achieved with informed consent, following the Declaration of Helsinki. The study was approved by the Brent Research Ethics Committee. DARC spots were identified from DARC retinal images obtained at 120 minutes after ANX776 administration in ON (n = 12) and healthy (n = 68) subjects using the CNN-aided algorithm. DM characteristics including the mean, std (standard deviation), skew, kurtosis, cv (coefficient of variation), bvdist (Euclidean distance from the nearest blood vessel) and the 7 Hu moments, were analysed on DARC spots from ON (n = 357) and healthy (n = 2457) eyes using two-tailed student t-tests. The distributions of individual spot metrics were then analysed using the two-sample Kolmogorov-Smirnov (KS) test.

Results : The mean, cv and kurtosis (p ≤ 0.0001) and hu1 and std (p ≤ 0.001) were found to be significantly different (using the two-sided t-test) between DARC spots in ON and healthy controls. The distributions of the individual metrics analysed using the two-sample KS test, showed a significant difference (p ≤ 0.0001) between ON and healthy spots for the same variables (mean, cv, kurtosis, hu1 and std).

Conclusions : Our findings indicate that mean, cv, kurtosis, hu1 and std DM analysis of automatically identified DARC spots, can be used to distinguish DARC spots in ON and healthy subjects. Further planned trials with larger ON groups will involve DMs in the analysis between ON and healthy controls; these early results support use of DARC for ON diagnosis.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

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