June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Detection of retinal fluids in OCT scans by an automated deep learning algorithm compared to human expert grading in the HAWK & HARRIER trials
Author Affiliations & Notes
  • Hrvoje Bogunovic
    Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • John Seaman
    Novartis, Switzerland
  • Philippe Margaron
    Novartis, Switzerland
  • Philipp Seeböck
    Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Bianca S S. Gerendas
    Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Daniel Lorand
    Novartis, Switzerland
  • Guillaume Normand
    Novartis, Switzerland
  • Ursula Schmidt-Erfurth
    Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
  • Footnotes
    Commercial Relationships   Hrvoje Bogunovic, None; John Seaman, Novartis (E); Philippe Margaron, Novartis (E); Philipp Seeböck, None; Bianca S Gerendas, IDx (F), Novartis (C), Roche (C); Daniel Lorand, Novartis (E); Guillaume Normand, Novartis (E); Ursula Schmidt-Erfurth, Böhringer Ingelheim (C), Genentech (C), Novartis (C), Roche (C)
  • Footnotes
    Support  Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, and Novartis AG
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 5187. doi:
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      Hrvoje Bogunovic, John Seaman, Philippe Margaron, Philipp Seeböck, Bianca S S. Gerendas, Daniel Lorand, Guillaume Normand, Ursula Schmidt-Erfurth; Detection of retinal fluids in OCT scans by an automated deep learning algorithm compared to human expert grading in the HAWK & HARRIER trials. Invest. Ophthalmol. Vis. Sci. 2020;61(7):5187.

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

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Abstract

Purpose : To evaluate the performance of an automated deep learning algorithm in identifying retinal fluids in OCT scans of patients treated for neovascular age-related macular degeneration (nAMD).

Methods : OCT scans acquired with routinely-used devices - Cirrus-Zeiss Meditec, Spectralis-Heidelberg Engineering and Topcon, were analyzed using a previously described deep learning algorithm in order to derive intraretinal fluid (IRF) and subretinal fluid (SRF) volumes, and hence fluid presence, in the 1, 3 and 6mm areas within the macula. These volumes were compared to expert grading of fluid status (presence/absence) from a centralized reading center using the area under the curve (AUC) of receiver operating characteristic as a measure of concordance.

Results : 19,034 OCT scans were analyzed from monthly visits over 2 years in HARRIER. In the central 1 mm, the concordance for the detection of IRF and SRF between the algorithm and grading reached an AUC of 0.93 and 0.87, respectively. In the central 6 mm, the concordance for both IRF and SRF had an AUC of 0.90. Regarding IRF in the central 1mm, 18% of scans graded with no fluid were found to have fluid by the algorithm, while 8% of scans graded to have IRF had no fluid identified by the algorithm. Regarding SRF in the central 1mm, 23% of scans graded with no fluid were found to have fluid by the algorithm, while 15% of scans graded to have SRF had no fluid identified by the algorithm. Across the fluid types, the highest concordance was achieved on scans from Spectralis (AUC=0.92), while Topcon and Cirrus had a slightly lower concordance (AUC=0.89 and 0.86). Results from HAWK were analysed in the same manner.

Conclusions : Identification of IRF and SRF from deep learning-based segmentation of retinal fluid showed high concordance with expert grading. Furthermore, automated quantification of nAMD features across the entire macula, beyond retinal thickness measurements, illustrates the potential of such approaches to objectively analyze OCT scans in order to support the treatment decision in nAMD patients in clinical practice.

This is a 2020 ARVO Annual Meeting abstract.

 

AUCs showing the concordance between deep learning algorithm and expert grading for IRF (a) and SRF (b).

AUCs showing the concordance between deep learning algorithm and expert grading for IRF (a) and SRF (b).

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