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Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.

To investigate the association between manually measured retinal fluid heights and AI-quantified retinal fluid volumes and to explore disease activity indicated by fluid volume distributions in neovascular age-related macular degeneration (nAMD) using an approved AI-based algorithm. This retrospective study analyzed baseline OCT data from a multicenter nAMD cohort. Manually measured maximum macular fluid heights on B-scans in the central millimeter (CMM) for intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) were compared with vertical fluid heights and three-dimensional volumes obtained by automated quantification using an AI-based tool (RetInSight Fluid Monitor Version 2). Out of 890 eyes, IRF was identified in the CMM in 328 eyes, SRF in 502 eyes, and PED in 705 eyes. The correlation between manual and AI-based height measurements was strong for IRF (r = 0.87) and PED (r = 0.91), and moderate for SRF (r = 0.67). Manual height–AI volume correlations within the CMM were strong for IRF (r = 0.76) and PED (r = 0.87), and moderate for SRF (r = 0.55). These correlations decreased when total fluid volumes within the central 6 mm were compared with manual CMM height, indicating that CMM height does not represent total nAMD disease activity. In conclusion, AI-derived and manual height measurements showed strong agreement for IRF and PED and moderate agreement for SRF. Maximum fluid height did not reliably reflect overall fluid volume or spatial distribution. AI-based retinal fluid assessment enables quantitative, whole-volume evaluation of retinal fluid and provides a measure of nAMD disease activity.

This proof-of-concept study developed and validated an automated, biomarker-targeted, real-time microperimetry (MP) approach for individualized structure-function assessment in geographic atrophy (GA). Patients underwent volumetric spectral-domain optical coherence tomography (OCT) and mesopic MP. Automated deep learning-based OCT segmentation using a clinically validated software was applied to identify regions of ellipsoid zone loss (EZL) and retinal pigment epithelium loss (RPEL). A rule-based algorithm then selected 40 biomarker-targeted retinal test locations, with test-point density dynamically adapted to individual lesion morphology and deliberately prioritized within EZL rather than uniformly distributed. Statistical analysis employed multivariable mixed-effects models. In 64 eyes from 44 patients (mean age 79.1 ± 5.0 years; 59% female), 2,560 targeted MP stimuli were tested. Mean retinal sensitivity (RS) was 7.2 ± 7.4 dB in RPEL, 13.3 ± 7.1 dB in EZL, and 19.0 ± 5.0 dB in preserved retina ( p  < 0.001). The median deviation between intended and actual test locations was 38.2 μm (IQR 24.6–59.3 μm), with 98.5% of points correctly localized. This OCT-based, biomarker-guided MP approach enables precise, patient-specific structure-function mapping within a single visit and may serve as a sensitive functional endpoint in future GA trials and clinical practice.

To investigate the impact of retinal fluid dynamics on visual outcomes in patients with treatment-naïve neovascular age-related macular degeneration (nAMD) treated in the real world over 5 years using approved AI-based fluid monitoring. Real-world data comprising OCT scans and electronic medical records from 148 patients (187 eyes) were extracted from the Fight Retinal Blindness! (FRB! ) Zürich database. OCT scans were analysed using an approved AI algorithm (RetInSight, Vienna, Austria) to quantify fluid volumes by compartements. The impact of fluid persistence and fluctuations on BCVA change was assessed using forward stepwise regression and mixed models. Fluid compartments were further categorized into quartiles (SD-Qs), and the effect of fluid fluctuations on BCVA analysed (SD-Q1 least and SD-Q4 greatest variability of fluctuations). The greatest PED fluctuations in the central 1-mm showed an accentuated BCVA decrease after 2 and 4 years (estimate: -0.07, P  = 0.019; estimate: -0.15, P  < 0.01). After 4 years, eyes in SD-Q4 compared with SD-Q1 with greater PED fluctuations in the central 1-mm and 6-mm area were affected by a significant mean reduction in BCVA (-5.7 letters ( P  = 0.013); -6.1 letters ( P  = 0.015)). Greater intraretinal fluid (IRF) fluctuations (central 1-mm) (SD-Q4 compared with SD-Q1) were associated with a significantly worse mean BCVA by -6.8 letters ( P  = 0.018) after 5 years. Fluid persistence was not associated with statistically significant BCVA changes. In routine clinical management of nAMD, greater fluctuations of PED and IRF correlate with worse BCVA outcomes over long-term follow-up. A well-suited treatment regimen is required in the real world which can be utilized with AI-based fluid monitoring.

This study compared an automated deep learning algorithm with certified human graders from the Vienna Reading Center (VRC) in identifying intra- (IRF) and subretinal fluid (SRF) in OCT scans of patients treated for neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME) and branch retinal vein occlusion (BRVO). Multicenter clinical trial data from the VRC imaging database was used for this post hoc analysis. OCT scans were analyzed using a validated algorithm (RetInSight, Vienna, Austria) to compute IRF and SRF volumes. These fluid volumes were compared to fluid presence graded by trained and experienced graders of the VRC. 6898 OCT scans were analyzed for fluid volumes and presence of IRF and SRF. For nAMD/DME /BRVO in the central millimeter: the overall concordance for the detection of IRF and SRF between the algorithm and manual grading reached an AUC of 0.94/0.92/0.98 and 0.89/0.95/0.92, respectively. This deep learning approach showed a high concordance with human expert grading for detection of IRF and SRF and provides precise volumetric information across different retinal fluid-associated diseases. Thus, automated fluid quantification is a feasible tool for standardized treatment decision support and disease monitoring in clinical practice at the highest human expert level.

A fully discrete Galerkin scheme for a thermodynamically consistent transient Maxwell–Stefan system for the mass particle densities, coupled to the Poisson equation for the electric potential, is investigated. The system models the diffusive dynamics of an isothermal ionized fluid mixture with vanishing barycentric velocity. The equations are studied in a bounded domain, and different molar masses are allowed. The Galerkin scheme preserves the total mass, the nonnegativity of the particle densities, their boundedness and satisfies the second law of thermodynamics in the sense that the discrete entropy production is nonnegative. The existence of solutions to the Galerkin scheme and the convergence of a subsequence to a solution to the continuous system is proved. Compared to previous works, the novelty consists in the treatment of the drift terms involving the electric field. Numerical experiments show the sensitive dependence of the particle densities and the equilibration rate on the molar masses.

AimTo predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real-world cohort.MethodsSpectral-domain optical coherence tomography data of 158 treatment-naïve patients with nAMD from the Fight Retinal Blindness! registry in Zurich were processed at baseline, and after initial treatment using intravitreal anti-VEGF to predict subsequent 1-year and 4-year outcomes. Intraretinal and subretinal fluid and pigment epithelial detachment volumes were segmented using a deep learning algorithm (Vienna Fluid Monitor, RetInSight, Vienna, Austria). A predictive machine learning model for future treatment requirements and morphological outcomes was built using the computed set of quantitative features.ResultsTwo hundred and two eyes from 158 patients were evaluated. 107 eyes had a lower median (≤7) and 95 eyes had an upper median (≥8) number of injections in the first year, with a mean accuracy of prediction of 0.77 (95% CI 0.71 to 0.83) area under the curve (AUC). Best-corrected visual acuity at baseline was the most relevant predictive factor determining final visual outcomes after 1 year. Over 4 years, half of the eyes had progressed to macular atrophy (MA) with the model being able to distinguish MA from non-MA eyes with a mean AUC of 0.70 (95% CI 0.61 to 0.79). Prediction for subretinal fibrosis reached an AUC of 0.74 (95% CI 0.63 to 0.81).ConclusionsThe regulatory approved AI-based fluid monitoring allows clinicians to use automated algorithms in prospectively guided patient treatment in AMD. Furthermore, retinal fluid localisation and quantification can predict long-term morphological outcomes.

Background/Objectives To analyse short-term changes of mean photoreceptor thickness (PRT) on the ETDRS-grid after vitrectomy and membrane peeling in patients with epiretinal membrane (ERM). Subjects/Methods Forty-eight patients with idiopathic ERM were included in this prospective study. Study examinations comprised best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) before surgery, 1 week (W1), 1 month (M1) and 3 months (M3) after surgery. Mean PRT was assessed using an automated algorithm and correlated with BCVA and central retinal thickness (CRT). Results Regarding PRT changes of the study eye in comparison to baseline values, a significant decrease at W1 in the 1 mm, 3 mm and 6 mm area (all p -values < 0.001), at M1 ( p  = 0.009) and M3 ( p  = 0.019) in the central 1 mm area, a significant increase at M3 in the 6 mm area ( p  < 0.001), but no significant change at M1 in the 3 mm and 6 mm area and M3 in the 3 mm area (all p -values > 0.05) were observed. BCVA increased significantly from baseline to M3 (0.3LogMAR-0.15LogMAR, Snellen equivalent = 20/40-20/28 respectively; p  < 0.001). There was no correlation between baseline PRT and BCVA at any visit after surgery, nor between PRT and BCVA at any visit (all p -values > 0.05). Decrease in PRT in the 1 mm ( p  < 0.001), 3 mm ( p  = 0.013) and 6 mm ( p  = 0.034) area after one week correlated with the increase in CRT (449.9 µm–462.2 µm). Conclusions Although the photoreceptor layer is morphologically affected by ERMs and after their surgical removal, it is not correlated to BCVA. Thus, patients with photoreceptor layer alterations due to ERM may still benefit from surgery and achieve good functional rehabilitation thereafter.

Aims Age-related macular degeneration (AMD) is characterised by a progressive loss of central vision. Intermediate AMD is a risk factor for progression to advanced stages categorised as geographic atrophy (GA) and neovascular AMD. However, rates of progression to advanced stages vary between individuals. Recent advances in imaging and computing technologies have enabled deep phenotyping of intermediate AMD. The aim of this project is to utilise machine learning (ML) and advanced statistical modelling as an innovative approach to discover novel features and accurately quantify markers of pathological retinal ageing that can individualise progression to advanced AMD. Methods The PINNACLE study consists of both retrospective and prospective parts. In the retrospective part, more than 400,000 optical coherent tomography (OCT) images collected from four University Teaching Hospitals and the UK Biobank Population Study are being pooled, centrally stored and pre-processed. With this large dataset featuring eyes with AMD at various stages and healthy controls, we aim to identify imaging biomarkers for disease progression for intermediate AMD via supervised and unsupervised ML. The prospective study part will firstly characterise the progression of intermediate AMD in patients followed between one and three years; secondly, it will validate the utility of biomarkers identified in the retrospective cohort as predictors of progression towards late AMD. Patients aged 55–90 years old with intermediate AMD in at least one eye will be recruited across multiple sites in UK, Austria and Switzerland for visual function tests, multimodal retinal imaging and genotyping. Imaging will be repeated every four months to identify early focal signs of deterioration on spectral-domain optical coherence tomography (OCT) by human graders. A focal event triggers more frequent follow-up with visual function and imaging tests. The primary outcome is the sensitivity and specificity of the OCT imaging biomarkers. Secondary outcomes include sensitivity and specificity of novel multimodal imaging characteristics at predicting disease progression, ROC curves, time from development of imaging change to development of these endpoints, structure-function correlations, structure-genotype correlation and predictive risk models. Conclusions This is one of the first studies in intermediate AMD to combine both ML, retrospective and prospective AMD patient data with the goal of identifying biomarkers of progression and to report the natural history of progression of intermediate AMD with multimodal retinal imaging.