Incomplete Retinal Pigment Epithelial and Outer Retinal Atrophy

Wu, Zhichao; Goh, Kai Lyn; Hodgson, Lauren; Guymer, Robyn H.

doi:10.1016/j.ophtha.2022.09.004

Cited by 28 publications

(33 citation statements)

References 29 publications

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“…For instance, Wu et al found that incomplete retinal pigment epithelium and outer retinal atrophy did not contribute any greater predictive power beyond that offered by nascent geographic atrophy in predicting GA progression. Indeed, only 3% of observed eyes with incomplete retinal pigment epithelium and outer retinal atrophy progressed to GA within 36 months in the study, compared to 93.1% of eyes with incomplete retinal pigment epithelium and outer retinal atrophy in the initial study . These discrepant results may be due in part to the challenging of achieving interreader agreement for incomplete retinal pigment epithelium and outer retinal atrophy even among experts.…”

Section: Discussionmentioning

confidence: 86%

“…Recent publications have also used deep learning to automatically detect pre-GA lesions, like incomplete retinal pigment epithelium and outer retinal atrophy or nascent GA, from SD-OCT images. However, the predictive utility and reproducibility of these markers for progression to GA remains in contention . For instance, Wu et al found that incomplete retinal pigment epithelium and outer retinal atrophy did not contribute any greater predictive power beyond that offered by nascent geographic atrophy in predicting GA progression.…”

Section: Discussionmentioning

confidence: 99%

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A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

Dow,

Jeong,

Katz

et al. 2023

JAMA Ophthalmol

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ImportanceThe identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network–based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans.ObjectiveTo develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan.Design, Setting, and ParticipantsThis retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023.ExposureA position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3).Main Outcomes and MeasuresPrediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.ResultsThe study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3).Conclusions and RelevanceThe fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

Dow,

Jeong,

Katz

et al. 2023

JAMA Ophthalmol

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“…Depending on the size of the lesions, smaller or greater than 250 μm incomplete or complete terms were used, respectively 2 . Recently iRORA has been proposed as a biomarker of intermediate AMD, however, as Wu et al suggested, iRORA is also a precursor of cRORA and therefore a predictive factor for GA development 57 . Furthermore, Flores et al identified iRORA as the factor with the higher probability of developing advanced AMD (OR 12.91) 58 …”

Section: Biomarkersmentioning

confidence: 99%

Optical coherence tomography biomarkers in early and intermediate age‐related macular degeneration: A clinical guide

Vidal‐Oliver,

Montolío‐Marzo,

Gallego‐Pinazo

et al. 2024

Clinical Exper Ophthalmology

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Advanced forms of age‐related macular degeneration (AMD), characterised by atrophic and neovascular changes, are a leading cause of vision loss in the elderly population worldwide. Prior to the development of advanced AMD, a myriad of risk factors from the early and intermediate stages of AMD have been published in the scientific literature over the last years. The ability to precisely recognise structural and anatomical changes in the ageing macula, altogether with the understanding of the individual risk implications of each one of them is key for an accurate and personalised diagnostic assessment. The present review aims to summarise updated evidence of the relative risk conferred by diverse macular signs, commonly seen on optical coherence tomography, in terms of progression to geographic atrophy or macular neovascularization. This information may also serve as a basis for tailored follow‐up monitoring visits.

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“…Assessment of iRORA and cRORA has been demonstrated as having consistent inter-reader agreement, and thus may be useful not only in clinical setting but also in interventional trials in AMD [16 ▪▪ ]. Wu et al [17 ▪ ] concurred that iRORA is a significant risk factor for progression to GA, but the association is likely driven by the development of specific features that define nascent geographic atrophy (nGA) – including subsidence of the inner nuclear layer and outer plexiform layer, or the presence of a hyporeflective wedge-shaped band within Henle's fiber layer. Persistent choroidal hypertransmission defects on OCT imaging are also strongly associated with nGA [12,18].…”

Section: Early Detection Of Geographic Atrophymentioning

confidence: 99%

Identifying geographic atrophy

Clevenger

Rachitskaya

2023

Current Opinion in Ophthalmology

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Purpose of reviewAge-related macular degeneration (AMD) is one of the leading causes of blindness and can progress to geographic atrophy (GA) in late stages of disease. This review article highlights recent literature which assists in the accurate and timely identification of GA, and monitoring of GA progression. Recent findingsTechnology for diagnosing and monitoring GA has made significant advances in recent years, particularly regarding the use of optical coherence tomography (OCT). Identification of imaging features which may herald the development of GA or its progression is critical. Deep learning applications for OCT in AMD have shown promising growth over the past several years, but more prospective studies are needed to demonstrate generalizability and clinical utility.

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Incomplete Retinal Pigment Epithelial and Outer Retinal Atrophy

Cited by 28 publications

References 29 publications

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

Optical coherence tomography biomarkers in early and intermediate age‐related macular degeneration: A clinical guide

Identifying geographic atrophy

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