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Purpose: To systematically review and report the rate of exudative progression over time in patients with nonexudative macular neovascularization (MNV) in age-related macular degeneration (AMD)., Design: Systematic review with prevalence meta-analyses and individual participant meta-analysis., Methods: We searched 10 literature databases on March 26, 2023, for studies of consecutive patients with treatment-naïve nonexudative MNV in AMD. The primary outcome of interest was time from diagnosis to exudative progression. We conducted meta-analyses on the prevalence of exudative progression at 1 and 2 years. Where possible, we extracted individual participant data from studies and conducted an individual participant meta-analysis and explored the exudative progression using a time-to-event curve., Results: We identified 16 eligible studies with a total of 384 eyes with nonexudative MNV. Exudative progression had occurred in 20.9% (95% CI 13.1%-29.8%) of eyes at 1 year and in 30.7% (95% CI 21.8%-40.4%) at 2 years. Similar results were observed in the individual participant meta-analysis, showing exudative progression in 18.9% (95% CI 13.5%-26.3%) of eyes at 1 year and 31.3% (95% CI 24.2%-40.0%) at 2 years. Risk factors for a fast exudative progression were the presence of subretinal lipid globules, large MNV areas, rapid MNV growth, growth in pigment epithelium detachment height and width, appearance of a branching pattern, and development of a hyporeflective halo around the MNV., Conclusions: Nonexudative MNVs in AMD are at high risk of exudative progression. Recognition of these lesions may allow for better individualized follow-up regimens in which closer monitoring may facilitate earlier diagnosis of exudative progression., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: In this study, we investigate the potential of a novel artificial intelligence-based system for autonomous follow-up of patients treated for neovascular age-related macular degeneration (AMD)., Methods: A temporal deep learning model was trained on a data set of 84 489 optical coherence tomography scans from AMD patients to recognize disease activity, and its performance was compared with a published non-temporal model trained on the same data (Acta Ophthalmol, 2021). An autonomous follow-up system was created by augmenting the AI model with deterministic logic to suggest treatment according to the observe-and-plan regimen. To validate the AI-based system, a data set comprising clinical decisions and imaging data from 200 follow-up consultations was collected prospectively. In each case, both the autonomous AI decision and original clinical decision were compared with an expert panel consensus., Results: The temporal AI model proved superior at detecting disease activity compared with the model without temporal input (area under the curve 0.900 (95% CI 0.894-0.906) and 0.857 (95% CI 0.846-0.867) respectively). The AI-based follow-up system could make an autonomous decision in 73% of the cases, 91.8% of which were in agreement with expert consensus. This was on par with the 87.7% agreement rate between decisions made in the clinic and expert consensus (p = 0.33)., Conclusions: The proposed autonomous follow-up system was shown to be safe and compliant with expert consensus on par with clinical practice. The system could in the future ease the pressure on public ophthalmology services from an increasing number of AMD patients., (© 2022 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation.)