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Reliability of retinal pathology quantification in age-related macular degeneration: Implications for clinical trials and machine learning applications

Reliability of retinal pathology quantification in age-related macular degeneration: Implications for clinical trials and machine learning applications
Reliability of retinal pathology quantification in age-related macular degeneration: Implications for clinical trials and machine learning applications

Purpose: to investigate the interreader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting. 

Methods: in this cross-sectional case series, spectral-domain optical coherence tomog-raphy (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovas-cular AMD (56 treatment naive, 56 after three anti–vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform. Dice score, Bland-Altman plots, coefficients of repeatability, coefficients of variation, and intraclass correlation coefficients were assessed. 

Results: loss of ellipsoid zone, pigment epithelium detachment, subretinal fluid, and drusen were the most abundant features in our cohort. Subretinal fluid, intraretinal fluid, hypertransmission, descent of the outer plexiform layer, and pigment epithelium detachment showed highest interreader agreement, while detection and measures of loss of ellipsoid zone and retinal pigment epithelium were more variable. The agreement on the size and location of the respective annotation was more consistent throughout all features. 

Conclusions: the interreader agreement depended on the respective OCT-based feature. A selection of reliable features might provide suitable surrogate markers for disease progression and possible treatment effects focusing on different disease stages. Translational Relevance: This might give opportunities for a more time-and cost-effective patient assessment and improved decision making as well as have implications for clinical trials and training machine learning algorithms.

Agreement, AMD, Annotation, Artificial intelligence, Deep learning, Imaging, Interrater, Interreader, Machine learning, OCT, Optical coherence tomography, Retina
Müller, Philipp L.
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Liefers, Bart
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Treis, Tim
a39f852e-fb5f-4957-ad4e-168c6f6dd2cd
Rodrigues, Filipa Gomes
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Olvera-Barrios, Abraham
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Paul, Bobby
c56f72a7-43ac-4a95-8d4b-11155ba20484
Dhingra, Narendra
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Lotery, Andrew
5ecc2d2d-d0b4-468f-ad2c-df7156f8e514
Bailey, Clare
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Taylor, Paul
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Sánchez, Clarisa I.
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Tufail, Adnan
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Müller, Philipp L.
9d4f2df8-dd03-4c07-bb6a-395becee6246
Liefers, Bart
859fc392-f6e8-42d5-a130-96203abf6d37
Treis, Tim
a39f852e-fb5f-4957-ad4e-168c6f6dd2cd
Rodrigues, Filipa Gomes
5efd70f8-8cb2-408e-ac44-4aa2d16ff971
Olvera-Barrios, Abraham
60d930b7-35c5-4e83-aadc-59f49f56d470
Paul, Bobby
c56f72a7-43ac-4a95-8d4b-11155ba20484
Dhingra, Narendra
960466f9-2fe7-4762-af2c-e470a017e1b8
Lotery, Andrew
5ecc2d2d-d0b4-468f-ad2c-df7156f8e514
Bailey, Clare
f86729be-c141-4085-8acf-ef90a889e5a4
Taylor, Paul
648ece00-6e9a-433d-bc21-e5007de25029
Sánchez, Clarisa I.
939bf8bb-20eb-4d85-8496-372bc21167ea
Tufail, Adnan
4370b3b4-906d-4fe5-92bd-e6207fa1d59b

Müller, Philipp L., Liefers, Bart, Treis, Tim, Rodrigues, Filipa Gomes, Olvera-Barrios, Abraham, Paul, Bobby, Dhingra, Narendra, Lotery, Andrew, Bailey, Clare, Taylor, Paul, Sánchez, Clarisa I. and Tufail, Adnan (2021) Reliability of retinal pathology quantification in age-related macular degeneration: Implications for clinical trials and machine learning applications. Translational Vision Science and Technology, 10 (3), [4]. (doi:10.1167/tvst.10.3.4).

Record type: Article

Abstract

Purpose: to investigate the interreader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting. 

Methods: in this cross-sectional case series, spectral-domain optical coherence tomog-raphy (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovas-cular AMD (56 treatment naive, 56 after three anti–vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform. Dice score, Bland-Altman plots, coefficients of repeatability, coefficients of variation, and intraclass correlation coefficients were assessed. 

Results: loss of ellipsoid zone, pigment epithelium detachment, subretinal fluid, and drusen were the most abundant features in our cohort. Subretinal fluid, intraretinal fluid, hypertransmission, descent of the outer plexiform layer, and pigment epithelium detachment showed highest interreader agreement, while detection and measures of loss of ellipsoid zone and retinal pigment epithelium were more variable. The agreement on the size and location of the respective annotation was more consistent throughout all features. 

Conclusions: the interreader agreement depended on the respective OCT-based feature. A selection of reliable features might provide suitable surrogate markers for disease progression and possible treatment effects focusing on different disease stages. Translational Relevance: This might give opportunities for a more time-and cost-effective patient assessment and improved decision making as well as have implications for clinical trials and training machine learning algorithms.

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Accepted/In Press date: 22 December 2020
Published date: 1 March 2021
Additional Information: Funding Information: The authors thank the members of the CAM group for setting the standards of the feature grading for this work. This work was supported by the German Research Foundation (grant MU4279/2-1 to PLM), the United Kingdom’s National Institute for Health Research of Health’s Biomedical Research Centre for Ophthalmology at Moorfields Eye Hospital, and UCL Institute of Ophthalmology. The views expressed are those of the authors and not necessarily those of the Department of Health. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; prepa- ration, review, or approval of the manuscript; and decision to submit the manuscript for publication. Publisher Copyright: © 2021 The Authors. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Agreement, AMD, Annotation, Artificial intelligence, Deep learning, Imaging, Interrater, Interreader, Machine learning, OCT, Optical coherence tomography, Retina

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Local EPrints ID: 450692
URI: http://eprints.soton.ac.uk/id/eprint/450692
PURE UUID: de432216-9194-4fb2-bc84-02c301ee00c0
ORCID for Andrew Lotery: ORCID iD orcid.org/0000-0001-5541-4305

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Date deposited: 06 Aug 2021 16:31
Last modified: 18 Mar 2024 02:57

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Contributors

Author: Philipp L. Müller
Author: Bart Liefers
Author: Tim Treis
Author: Filipa Gomes Rodrigues
Author: Abraham Olvera-Barrios
Author: Bobby Paul
Author: Narendra Dhingra
Author: Andrew Lotery ORCID iD
Author: Clare Bailey
Author: Paul Taylor
Author: Clarisa I. Sánchez
Author: Adnan Tufail

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