A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)

Normando, Eduardo; Yap, Timothy E.; Maddison, John; Miodragovic, Serge; Bonetti, Paolo; Almonte, Melanie; Mohammad, Nada G.; Ameen, Sally; Crawley, Laura; Ahmed, Faisal; Bloom, Philip; Cordeiro, M. Francesca

doi:10.1080/14737159.2020.1758067

Cited by 37 publications

(44 citation statements)

References 51 publications

(38 reference statements)

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“…Using fluorescently labeled Annexin, Cordeiro et al have demonstrated the ability to visualize apoptosis of individual RGCs in experimental models of neurodegeneration and in humans with glaucoma 61. DARCs may be able to register early stages of RGC pathology, although it is not well established whether DARCs can detect changes in RNFL thickness reliably earlier than OCT62 ; however, recent published findings from the Phase 2 DARC trial demonstrate it to predict RNF loss by 18 months in glaucoma eyes 73.…”

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confidence: 99%

Retinal imaging in Alzheimer's and neurodegenerative diseases

Snyder

Alber

Alt

et al. 2020

Alzheimer's & Dementia

119

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In the last 20 years, research focused on developing retinal imaging as a source of potential biomarkers for Alzheimer's disease and other neurodegenerative diseases, has increased significantly. The Alzheimer's Association and the Alzheimer's & Dementia: Diagnosis, Assessment, Disease Monitoring editorial team (companion journal to Alzheimer's & Dementia) convened an interdisciplinary discussion in 2019 to identify a path to expedite the development of retinal biomarkers capable of identifying This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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confidence: 99%

Retinal imaging in Alzheimer's and neurodegenerative diseases

Snyder

Alber

Alt

et al. 2020

Alzheimer's & Dementia

119

101

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“…Moreover, the magnitude of SRF was related to that of the DARC count, and this is attributed to the mechanism by which DARC identifies an early endothelial angiogenic activity, as seen for the first time in vivo in a rabbit model of hVEGF-induced angiogenesis/leakage. This is the first time DARC technology has been reported in a disease other than glaucoma and suggests its broader applicability as a biomarker of retinal activity [ 17 ] [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…For analysis of DARC images, the newly described and unmodified DARC CNN algorithm was used to identify DARC spots [ 17 ]. Briefly, the DARC CNN algorithm comprised training a CNN to classify spots from a control dataset which was initially identified using template matching and manually classified by five observers.…”

Section: Methodsmentioning

confidence: 99%

“…We have recently described a retinal imaging technology that combines a CNN-aided algorithm with an intravenously administered novel biologic fluorescent marker, ANX776 (fluorescent-labeled annexin V) which binds to exposed phosphatidylserine on apoptotic and stressed cell membranes [ 17 ]. ANX776 positive-labeled cells are visualized as ‘white spots’ on the retina using the ICGA settings of a commonly utilized confocal scanning laser ophthalmoscope (cSLO) in a technology called DARC (Detection of Apoptosing Retinal Cells) [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology

Corazza

Maddison²,

Bonetti

et al. 2020

Expert Review of Molecular Diagnostics

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“…For instance, in 2018, the US Food and Drug Administration (FDA) approved an AI medical imaging device to detect certain diabetes-related eye problems [33]. Recently, the Wellcome Trust found a research developing an AI-aided method to detect glaucoma progression using Detection of Apoptosing Retinal Cells (DARC) 18 months earlier than the current gold standard method [34].…”

Section: Vertical Integration: Provide Customized Value-added Servicesmentioning

confidence: 99%

Transformation of the Taiwan Biobank 3.0: vertical and horizontal integration

2020

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Researchers expect a high quality of biospecimens/data and value-added services from biobanks. Therefore, the concept of "biobank 3.0" was introduced so that biobanks could better meet the needs of stakeholders and maintain sustainable operations. Theoretically, the Taiwan Biobank (TWB) has already gone through the concepts of biobank 1.0 and 2.0. However, three challenges still need to be addressed before it can be transformed into a new generation of the TWB (namely, the TWB 3.0): (1) the difficulty of integrating other biobanks' resources, (2) the efficiency and effectiveness of the release and use of biospecimens/data, and (3) the development of income and revenue models of sustainability. To address these issues, this paper proposes a framework for the TWB 3.0 transformation based on a dual-pillar approach composed of a "physically" vertical integration driven by the TWB and a "virtually" horizontal network led by the National Health Research Institutes (NHRI) of Taiwan. Using prominent biobanks such as the Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC), the UK Biobank, and the National Institutes of Health (NIH)'s All of Us Research Program as models, the TWB can strengthen its ongoing TWB 2.0 operations in regional and/or international collaboration, increase the value of data collected and develop closer relationships with biobank participants and users. To these ends, the authors highlight key issues that include, but are not limited to, the harmonization of relevant ELSI standards for various biobanks' integrations; the value-added services and the efficiency of Big Data Era related research and/or precision medicine development, and financial concerns related to biobank sustainability. This paper concludes by discussing how greater participant engagement and the uptake of Information Technology (IT) and Artificial Intelligence (AI) applications can be used in partnership with vertical and horizontal integration as part of a four-pronged approach to promote biobank sustainability, and facilitate the TWB 3.0 transformation.

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A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)

Cited by 37 publications

References 51 publications

Retinal imaging in Alzheimer's and neurodegenerative diseases

Retinal imaging in Alzheimer's and neurodegenerative diseases

Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology

Transformation of the Taiwan Biobank 3.0: vertical and horizontal integration

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