June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Evaluating an OCT-based Algorithm of Central Subfield Thickness Estimation on AMD and DME patients
Author Affiliations & Notes
  • Mathias Gallardo
    ARTORG Center, Universitat Bern, Bern, Bern, Switzerland
  • Oussama Habra
    Department for Ophthalmology, Inselspital, University Hospital, Bern, Switzerland
  • Till Meyer zu Westram
    ARTORG Center, Universitat Bern, Bern, Bern, Switzerland
  • Sandro De Zanet
    RetinAI Medical AG, Bern, Bern, Switzerland
  • Sebastian Wolf
    Department for Ophthalmology, Inselspital, University Hospital, Bern, Switzerland
  • Raphael Sznitman
    ARTORG Center, Universitat Bern, Bern, Bern, Switzerland
  • Martin Sebastian Zinkernagel
    Department for Ophthalmology, Inselspital, University Hospital, Bern, Switzerland
  • Footnotes
    Commercial Relationships   Mathias Gallardo None; Oussama Habra None; Till Meyer zu Westram None; Sandro De Zanet RetinAI Medical AG, Code E (Employment); Sebastian Wolf Bayer Healthcare Pharmaceuticals, Carl Zeiss Meditec, Chengdu Kanghong Biotechnology, Heidelberg Engineering, Novartis Pharmaceuticals Corporation, RetinAI Medical AG, Roche, Code C (Consultant/Contractor), Allergan, Bayer Healthcare Pharmaceuticals, Carl Zeiss Meditec, Heidelberg Engineering, Novartis Pharmaceuticals Corporation, Roche, Code F (Financial Support); Raphael Sznitman Haag-Streit AG, Code C (Consultant/Contractor), RetinAI Medical AG , Code I (Personal Financial Interest), Bayer AG, medupdate , Code R (Recipient); Martin Zinkernagel Allergan, Bayer, Novartis, Code C (Consultant/Contractor), Bayer, Boehringer Ingelheim, Code F (Financial Support), Novartis, Code I (Personal Financial Interest), Heidelberg Engineering, Zeiss, Code R (Recipient)
  • Footnotes
    Support  EUROSTARS grant (E!12712)
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2993 – F0263. doi:
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    • Get Citation

      Mathias Gallardo, Oussama Habra, Till Meyer zu Westram, Sandro De Zanet, Sebastian Wolf, Raphael Sznitman, Martin Sebastian Zinkernagel; Evaluating an OCT-based Algorithm of Central Subfield Thickness Estimation on AMD and DME patients. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2993 – F0263.

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

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Abstract

Purpose : To evaluate the accuracy of an algorithm to estimate Central Subfield Thickness (CST) from OCT volumes for patients with AMD or DME.

Methods : We collected OCT volumes from two groups of patients, respectively including exudative AMD (541) and DME (1’568) patients. We refer to them as the AMD group and DME group. All patients received an anti-VEGF treatment, were treated and monitored between 01/2013 and 06/2021. We found 3’974 OCT volumes for the AMD group and 11’501 OCT volumes for the DME group. The algorithm to be evaluated relies on the CE-marked Discovery® layer segmentation algorithm (RetinAI Medical AG, Switzerland) and computes the average retinal thickness in the central-1mm region from the ETDRS grid. For each OCT volume, the true CST was measured independently by two graders and a third grader in case of disagreement. The annotations were performed by the Bern Photographic Reading Center (Inselspital, Universitätsspital Bern, Universitätsklinik für Augenheilkunde, Bern, Switzerland). After removing the ungradable OCT volumes and considering the 99th percentile of the thicknesses, the AMD group comprises of 3’894 OCT volumes (537 patients) and the DME group of 11'269 OCT volumes (1’526 patients).

Results : We reported a strong correlation between the annotated values of CSTs and the predicted ones from the algorithm (R2=0.96) for both groups. We observed that the algorithm tends to over-estimate the retinal thickness in general, leading however to a small mean absolute error (prediction - annotation), 3.19μm (95% CI, 2.6–3.8μm) and 10.68μm (95% CI, 10.4–11μm) for AMD and DME groups, respectively. Indeed, relatively to the median annotated CSTs (302μm and 310μm), these errors represent 1.06% and 3.44% of the total retinal thickness for AMD and DME cohorts, respectively. We observed that 5.4% (210/3’894) samples are located outside the 95% limits of agreement for AMD and 5.1% (574/11 269) samples are located outside the 95% limits of agreement in DME.

Conclusions : We report very good performances for CST estimation on OCT volumes with AMD and DME. This unveils the potential of such algorithms to support clinical decision making and to envision new strategies to facilitate annotation for clinical trials. To understand the sources of the difference in CST errors between the two groups, we plan to analyze the ETDRS alignment and to consider the presence of fluids and other biomarkers.

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

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