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

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 tomography (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovascular 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.

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Purpose

The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression).

Methods

The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression).

Results

Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89.

Conclusions

Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds.

Translational Relevance

AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.

Developing a potential retinal OCT biomarker for local growth of geographic atrophy

Geographic atrophy (GA), the advanced stage of age-related macular degeneration, is a leading cause of blindness. GA lesions are characterized by anisotropic growth and the ability to predict growth patterns would be valuable in assessing potential therapeutics. In this study, we propose an OCT-based marker of local GA growth rate based on an axial projection of the OCT volume in the Henle fiber layer (HFL) and outer nuclear layer (ONL). We analyze the association between our proposed metric and local GA growth rates in a small longitudinal cohort of patients with AMD. These methods can potentially be used to identify risk markers, stratify patients, or assess response in future therapeutic studies.

Optical Coherence Tomography-Angiography in Geographic Atrophy

Geographic atrophy (GA) represents the non-exudative late stage of age-related macular degeneration and constitutes a leading cause of legal blindness in the developed world. It is characterized by areas of loss of outer retinal layers including photoreceptors, degeneration of the retinal pigment epithelium, and rarefication of the choriocapillaris. As all three layers are functionally connected, the precise temporal sequence and relative contribution of these layers towards the development and progression of GA is unclear. The advent of optical coherence tomography angiography (OCT-A) has allowed for three-dimensional visualization of retinal blood flow. Using OCT-A, recent studies have demonstrated that choriocapillaris flow alterations are particularly associated with the development of GA, exceed atrophy boundaries spatially, and are a prognostic factor for future GA progression. Furthermore, OCT-A may be helpful to differentiate GA from mimicking diseases. Evidence for a potential protective effect of specific forms of choroidal neovascularization in the context of GA has been reported. This article aims to give a comprehensive review of the current literature concerning the application of OCT-A in GA, and summarizes the opportunities and limitations with regard to pathophysiologic considerations, differential diagnosis, study design, and patient assessment.