Topographic Analysis of Photoreceptor Loss Correlated With Disease Morphology in Neovascular Age-Related Macular Degeneration

Riedl, Sophie; Cooney, Lewis; Grechenig, Christoph; Sadeghipour, Amir; Pablik, Eleonore; Seaman, John W.; Waldstein, Sebastian M.; Schmidt‐Erfurth, Ursula

doi:10.1097/iae.0000000000002717

Cited by 28 publications

(23 citation statements)

References 30 publications

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“…Apart from robust retinal layer segmentation and the precise spatial differentiation and quantification of retinal fluid compartments, [50][51][52] more refined biomarkers on a subclinical level, such as ellipsoid zone thickness or HRF, and the interaction of these biomarkers can now be explored to assess individual risk in disease progression. 16,[53][54][55][56] The possibilities in AI are continuously expanding and novel, clinically unknown biomarkers can be identified using unsupervised learning. The latter lets the AI algorithm detect retinal markers on its own and evaluates them for the prediction of disease progression, which widely opens the spectrum of pathologic feature detection.…”

Section: Discussionmentioning

confidence: 99%

Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression

Schmidt‐Erfurth

Bogunović

Grechenig

et al. 2020

American Journal of Ophthalmology

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To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth of GA.METHODS: Eyes with GA were prospectively included. Spectral-domain optical coherence tomography (SDOCT) and fundus autofluorescence images were acquired every 6 months. A 500-mm-wide junctional zone adjacent to the GA border was delineated and HRF were quantified using a validated DL algorithm. HRF concentrations in progressing and nonprogressing areas, as well as correlations between HRF quantifications and global and local GA progression, were assessed.RESULTS: A total of 491 SDOCT volumes from 87 eyes of 54 patients were assessed with a median followup of 28 months. Two-thirds of HRF were localized within a millimeter adjacent to the GA border. HRF concentration was positively correlated with GA progression in unifocal and multifocal GA (all P < .001) and de novo GA development (P [ .037). Local progression speed correlated positively with local increase of HRF (P value range <.001-.004). Global progression speed, however, did not correlate with HRF concentrations (P > .05). Changes in HRF over time did not have an impact on the growth in GA (P > .05).CONCLUSION: Advanced artificial intelligence (AI) methods in high-resolution retinal imaging allows to identify, localize, and quantify biomarkers such as HRF. Increased HRF concentrations in the junctional zone and future macular atrophy may represent progressive migration and loss of retinal pigment epithelium. AIbased biomarker monitoring may pave the way into the era of individualized risk assessment and objective decision-making processes. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

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Section: Discussionmentioning

confidence: 99%

Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression

Schmidt‐Erfurth

Bogunović

Grechenig

et al. 2020

American Journal of Ophthalmology

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“…In a post hoc analysis of the OCTAVE trial, Riedl and colleagues analyzed 185 eyes of 185 newly diagnosed treatment-naïve nAMD patients. ey showed a slight positive correlation between foveal EZ integrity and BCVA at baseline [44]. However, within the same study, BCVA variations and modifications in EZ integrity after anti-VEGF treatment did not show a meaningful correlation.…”

Section: Photoreceptor Layer Integritymentioning

confidence: 65%

“…Restoration of the foveal ELM band and the foveal EZ band after anti-VEGF therapy in nAMD has been described [40,42,44]. Restoration of the foveal ELM band after anti-VEGF therapy showed a correlation with better final BCVA.…”

Section: Photoreceptor Layer Integritymentioning

confidence: 93%

“…Various studies showed that foveal photoreceptor layer integrity is strongly correlated with visual acuity in several retinal diseases [35][36][37][38][39][40]. In nAMD, the disruption of the foveal ELM band [41][42][43] and the foveal EZ band [41,[43][44][45] has been associated with compromised BCVA at baseline and after anti-VEGF therapy.…”

Section: Photoreceptor Layer Integritymentioning

confidence: 99%

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OCT Biomarkers in Neovascular Age-Related Macular Degeneration: A Narrative Review

Metrangolo

Donati

Mazzola

et al. 2021

Journal of Ophthalmology

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Age-related macular degeneration (AMD) is the leading cause of legal blindness in elderly people. Neovascular AMD (nAMD) is responsible for the majority of cases of severe visual loss in eyes with AMD. Optical coherence tomography (OCT) is the most widely used technology for the diagnosis and follow-up of nAMD patients, which is widely used to study and guide the clinical approach, as well as to predict and evaluate treatment response. The aim of this review is to describe and analyze various structural OCT-based biomarkers, which have practical value during both initial assessment and treatment follow-up of nAMD patients. While central retinal thickness has been the most common and one of the first OCT identified biomarkers, today, other qualitative and quantitative biomarkers provide novel insight into disease activity and offer superior prognostic value and better guidance for tailored therapeutic management. The key importance of retinal fluid compartmentalization (intraretinal fluid, subretinal fluid, and subretinal pigment epithelium (RPE) fluid) will be discussed firstly. In the second part, the structural alterations of different retinal layers in various stages of the disease (photoreceptors layer integrity, hyperreflective dots, outer retinal tubulations, subretinal hyperreflective material, and retinal pigment epithelial tears) will be analyzed in detail. The last part of the review will focus on how alterations of the vitreoretinal interface (vitreomacular adhesion and traction) and of the choroid (sub-RPE hyperreflective columns, prechoroidal clefts, choroidal caverns, choroidal thickness and choroidal volume, and choroidal vascular index) interact with nAMD progression. OCT technology is evolving very quickly, and new retinal biomarkers are continuously described. This up-to-date review article provides a comprehensive description on how structural OCT-based biomarkers provide a valuable tool to monitor the progression of the disease and the treatment response in nAMD patients. Thus, in this perspective, clinicians will be able to allocate hospital resources in the best possible way and tailor treatment to the individual patient’s needs.

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“…The Notal OCT Analyzer [ 28 ] and Medical University of Vienna artificial intelligence-based Fluid Monitor [ 29 ] are fully automated tools for fluid detection and quantification on OCT images. These have facilitated quantitative measurements across multiple large datasets [ 30 ▪ ] and been applied to questions investigating retinal fluid measurements and visual outcomes [ 17 , 30 ▪ , 31 , 32 ▪ , 33 ▪ , 34 ▪ , 35 ], particularly to more precisely quantify and map changes in IRF and SRF over time.…”

Section: Development and Application Of Artificial Intelligence Models For Neovascular Age-related Macular Degenerationmentioning

confidence: 99%

Artificial intelligence-based predictions in neovascular age-related macular degeneration

Ferrara¹,

Newton²,

Lee

2021

Current Opinion in Ophthalmology

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Purpose of review Predicting treatment response and optimizing treatment regimen in patients with neovascular age-related macular degeneration (nAMD) remains challenging. Artificial intelligence-based tools have the potential to increase confidence in clinical development of new therapeutics, facilitate individual prognostic predictions, and ultimately inform treatment decisions in clinical practice. Recent findings To date, most advances in applying artificial intelligence to nAMD have focused on facilitating image analysis, particularly for automated segmentation, extraction, and quantification of imaging-based features from optical coherence tomography (OCT) images. No studies in our literature search evaluated whether artificial intelligence could predict the treatment regimen required for an optimal visual response for an individual patient. Challenges identified for developing artificial intelligence-based models for nAMD include the limited number of large datasets with high-quality OCT data, limiting the patient populations included in model development; lack of counterfactual data to inform how individual patients may have fared with an alternative treatment strategy; and absence of OCT data standards, impairing the development of models usable across devices. Summary Artificial intelligence has the potential to enable powerful prognostic tools for a complex nAMD treatment landscape; however, additional work remains before these tools are applicable to informing treatment decisions for nAMD in clinical practice.

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Topographic Analysis of Photoreceptor Loss Correlated With Disease Morphology in Neovascular Age-Related Macular Degeneration

Cited by 28 publications

References 30 publications

Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression

Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression

OCT Biomarkers in Neovascular Age-Related Macular Degeneration: A Narrative Review

Artificial intelligence-based predictions in neovascular age-related macular degeneration

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