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

IMPORTANCE: The 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. OBJECTIVE: To 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 PARTICIPANTS: This 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. EXPOSURE: A 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 MEASURES: Prediction 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. RESULTS: The 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 RELEVANCE: The 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

Age-Dependent Retinal Iron Accumulation and Degeneration in Hepcidin Knockout Mice

Hepcidin is an iron regulatory hormone expressed in the retina. In the present study, evidence from mice and tissue culture suggest that hepcidin is upregulated in response to increased retinal iron levels and normally serves to prevent retinal iron excess

Ceruloplasmin/Hephaestin Knockout Mice Model Morphologic and Molecular Features of AMD

PURPOSE: Iron is an essential element in human metabolism but also is a potent generator of oxidative damage with levels that increase with age. Several studies suggest that iron accumulation may be a factor in age-related macular degeneration (AMD). In prior studies, both iron overload and features of AMD were identified in mice deficient in the ferroxidase ceruloplasmin (Cp) and its homologue hephaestin (Heph) (double knockout, DKO). In this study, the location and timing of iron accumulation, the rate and reproducibility of retinal degeneration, and the roles of oxidative stress and complement activation were determined. METHODS: Morphologic analysis and histochemical iron detection by Perls' staining was performed on retina sections from DKO and control mice. Immunofluorescence and immunohistochemistry were performed with antibodies detecting activated complement factor C3, transferrin receptor, L-ferritin, and macrophages. Tissue iron levels were measured by atomic absorption spectrophotometry. Isoprostane F2α-VI, a specific marker of oxidative stress, was quantified in the tissue by gas chromatography/mass spectrometry. RESULTS: DKOs exhibited highly reproducible age-dependent iron overload, which plateaued at 6 months of age, with subsequent progressive retinal degeneration continuing to at least 12 months. The degeneration shared some features of AMD, including RPE hypertrophy and hyperplasia, photoreceptor degeneration, subretinal neovascularization, RPE lipofuscin accumulation, oxidative stress, and complement activation. CONCLUSIONS: DKOs have age-dependent iron accumulation followed by retinal degeneration modeling some of the morphologic and molecular features of AMD. Therefore, these mice are a good platform on which to test therapeutic agents for AMD, such as antioxidants, iron chelators, and antiangiogenic agents