Background/Objectives: Macular edema (ME), due to retinal vein occlusion (RVO), is a major cause of vision impairment. Many patients experience suboptimal responses to anti-vascular endothelial growth factor (anti-VEGF) monotherapy, necessitating alternative treatment approaches. Faricimab, a bispecific antibody targeting VEGF-A and angiopoietin-2 (Ang-2), introduces a novel dual-mechanism therapy. This study evaluates the short-term real-world efficacy of switching to Faricimab in patients with treatment-resistant ME secondary to RVO. Methods: This retrospective study included patients from LMU University Hospital who were switched to Faricimab due to an inadequate response or adverse events related to prior intravitreal therapy (Ranibizumab, Aflibercept, or Ozurdex^TM). All patients completed a structured loading phase of four monthly injections. Key outcome measures included changes in best-corrected visual acuity (BCVA, logMAR), central subfield thickness (CST, µm), and intraretinal fluid (IRF) presence on optical coherence tomography (OCT). Changes were assessed from baseline (mo0) to three months (mo3). Results: The study included 19 eyes from 19 patients (mean age 63.0 ± 14.2 years). BCVA improved from 0.20 logMAR at baseline to 0.00 logMAR at mo3 (p < 0.01). CST decreased from 325 µm to 280 µm (p < 0.01). The proportion of eyes with IRF reduced from 100% to 32% (p < 0.01). Significant reductions in retinal volume within the 1 mm and 6 mm (both p < 0.01) circles of the ETDRS grid were observed. Conclusions: Switching to Faricimab in patients resulted in significant short-term improvements in BCVA, CST, and IRF resolution. Given the small sample size and retrospective design, these findings should be interpreted as exploratory and hypothesis-generating. Further studies are needed to evaluate long-term efficacy and optimal treatment regimens.

Purpose: Age-related macular degeneration (AMD) remains the leading cause of blindness in developed countries. There are many different intravitreal anti-vascular endothelial growth factor (VEGF) drugs available for the treatment of neovascular AMD (nAMD). Unfortunately, not all patients respond equally well to the drugs, and some show recurrences during treatment. Since 01/2024, aflibercept 8 mg represents an additional treatment option and contains a four times higher dosage than the already known aflibercept 2 mg. Methods: To evaluate the real-world efficacy of aflibercept 8 mg in refractory nAMD patients, focusing on changes in key optical coherence tomography biomarkers over a follow-up period of the first four aflibercept 8 mg injections using a deep learning-based semantic segmentation algorithm. Inclusion criteria were: switch to aflibercept 8 mg after insufficient response to aflibercept 2 mg, marked by persistent retinal fluid or inability to extend treatment beyond 6 weeks; completion of at least 3 months (90 days) follow-up under treat-and-extend treatment regime; and no confounding conditions like intraocular infection, uveitis or other retinal diseases. Results: 23 eyes of 21 patients with therapy-resistant nAMD were switched to aflibercept 8 mg. All patients had previously received aflibercept 2 mg, with an average of 30.7 previous anti-VEGF injections. Significant reductions in intraretinal fluid and fibrovascular pigment epithelial detachment at timepoint V3 were observed. The decrease in subretinal fluid and central retinal thickness at V3 was not significant. Treatment intervals extended significantly by 24%, from a baseline average of 34 days to 42 days. Best-corrected visual acuity remained stable throughout the study period. Conclusions: Aflibercept 8 mg demonstrated significant efficacy and durability in reducing nAMD biomarkers and extending intervals in a real-world setting. The use of deep learning for biomarker quantification highlighted its potential for enhancing treatment monitoring and decision-making. Future studies with a larger patient cohort and prospective study setting should explore long-term outcomes and integration of artificial intelligence-driven analysis.

Background: Macular edema (ME) secondary to retinal vein occlusion (RVO) is a significant cause of vision impairment. Many patients show suboptimal responses to anti-vascular endothelial growth factor (anti-VEGF) monotherapy, prompting the exploration of alternative treatments. Faricimab is a bispecific antibody that targets VEGF-A and angiopoietin-2. We report 9-month real-world outcomes of switching to faricimab in therapy-resistant RVO-associated ME. Methods: In this retrospective study at a single tertiary center, patients with persistent or recurrent ME despite prior treatments (ranibizumab, aflibercept, or dexamethasone implant) were switched to faricimab. All eyes received a loading phase of four monthly faricimab injections, followed by a treat-and-extend regimen individualized per response. Key outcomes included best-corrected visual acuity (BCVA, logMAR), the central subfield thickness (CST, μm), and the intraretinal fluid (IRF) status on optical coherence tomography, assessed from the baseline (month 0, mo0) through the loading phase (mo1–mo3) and at month 9 (mo9). Results: Nineteen eyes (19 patients, mean age 64.8 years) were analyzed. The median BCVA improved from 0.20 to 0.00 logMAR by month 3 (p < 0.01) and was maintained at month 9. The median CST decreased from 325 μm at the baseline to 285 μm at month 3 (p < 0.01) and remained at 285 μm at month 9. IRF was present in 100% of eyes at the baseline, 26% at month 3, and 26% at month 9 (p < 0.01 for the baseline vs. month 9). Among eyes previously on anti-VEGF therapy (n = 14), the median treatment interval increased from 45.50 days at the baseline to 56.50 days at month 9 (p = 0.01; δ = 0.86). No intraocular inflammation or other adverse events were observed in this cohort over nine months. Conclusions: In this retrospective series, switching to faricimab was associated with improvements in vision and retinal anatomy that were maintained over 9 months; injection intervals were extended in a subset of eyes. These exploratory findings warrant confirmation in larger, controlled studies to define long-term effectiveness, safety, and dosing strategies.

Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. While current leading-edge transfer learning from pre-trained models can help with cases lacking data, it limits design choices, and generally results in the use of unnecessarily large models. We propose a novel, self-supervised training scheme for obtaining high-quality, pre-trained networks from unlabeled, cross-modal medical imaging data, which will allow for creating accurate and efficient models. We demonstrate this by accurately predicting optical coherence tomography (OCT)-based retinal thickness measurements from simple infrared (IR) fundus images. Subsequently, learned representations outperformed advanced classifiers on a separate diabetic retinopathy classification task in a scenario of scarce training data. Our cross-modal, three-staged scheme effectively replaced 26,343 diabetic retinopathy annotations with 1,009 semantic segmentations on OCT and reached the same classification accuracy using only 25% of fundus images, without any drawbacks, since OCT is not required for predictions. We expect this concept will also apply to other multimodal clinical data-imaging, health records, and genomics data, and be applicable to corresponding sample-starved learning problems.

Purpose: To determine real life quantitative changes in OCT biomarkers in a large set of treatment naive patients undergoing anti-VEGF therapy. For this purpose, we devised a novel deep learning based semantic segmentation algorithm providing, to the best of our knowledge, the first benchmark results for automatic segmentation of 11 OCT features including biomarkers that are in line with the latest consensus nomenclature of the AAO for age-related macular degeneration (AMD). Design: Retrospective study. Participants: Segmentation algorithm training set of 458 volume scans as well as single scans from 363 treatment naive patients for the analysis. Methods: Training of a Deep U-net based semantic segmentation ensemble algorithm leveraging multiple deep convolutional neural networks for state of the art semantic segmentation performance as well as analyzing OCT features prior to, after 3 and 12 months of anti-VEGF therapy. Main outcome measures: F1 score for the segmentation efficiency and the quantified volumes of 11 OCT features. Results: The segmentation algorithm achieved high F1 scores of almost 1.0 for neurosensory retina and subretinal fluid on a separate hold out test set with unseen patients. The algorithm performed worse for subretinal hyperreflective material and fibrovascular PED, on par with drusenoid PED and better in segmenting fibrosis. In the evaluation of treatment naive OCT scans, significant changes occurred for intraretinal fluid (mean: 0.03µm ³ to 0.01µm ³ , p<0.001), subretinal fluid (0.08µm ³ to 0.01µm ³ , p<0.001), subretinal hyperreflective material (0.02µm ³ to 0.01µm ³ , p<0.001), fibrovascular PED (0.12µm ³ to 0.09µm ³ , p=0.02) and central retinal thickness C0 (225.78µm ³ to 169.40µm ³ ).The amounts of intraretinal fluid, fibrovascular PED and ERM were predictive of poor outcome. Conclusions: The segmentation algorithm allows efficient volumetric analysis of OCT scans. Anti-VEGF therapy provokes most potent changes in the first 3 months and afterwards only acts as a stabilizing agent. Furthermore, a gradual loss of RPE hints at a progressing decline of visual acuity even beyond month 12. Additional research is required to understand how these accurate OCT predictions can be leveraged for a personalized therapy regimen. Précis: Novel high performance segmentation algorithm shows most volumetric changes under anti-VEGF therapy in oct biomarkers occur in the first 3 months. Afterwards the injections seem only to serve as a stabilizing agent.