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Purpose: To investigate the clinical outcomes of micropulse transscleral laser therapy (MP-TLT) in a cohort of glaucoma patients, including safety profile, post-operative transient intraocular pressure (IOP) spikes, long-term efficacy and prognostic factors in terms of IOP-lowering. Methods: This was a retrospective observational cohort study. Medical records of all patients who consecutively underwent MP-TLT between May 2019 and February 2023 at a tertiary referral centre were scrutinised and relevant data were retrospectively analysed. Results: A total of 131 patients (138 eyes) with a mean age of 73.2 ± 14.2 years were included. Mean pre-interventional IOP was 24.1 ± 9.1 mmHg. Within 6-12 h following the intervention on the same day, an IOP spike was regularly observed, reaching on average 31.7 ± 10.3 mmHg (p < 0.001 to baseline). Two years after the intervention, mean IOP was 16.1 ± 5.6 mmHg (p < 0.005 to baseline). In 18 eyes, the treatment was repeated, and the IOP lowering effect was more durable after the second intervention compared to the first one (Cox-Mantel test, p=<0.005). Apart from the transient post-interventional IOP spikes, no severe complications were observed. Conclusions: MP-TLT is associated with significant IOP spikes in the first post-operative hours. Thus, close post-interventional IOP monitoring or even preventive (additional) IOP-lowering treatment may be considered. In the long term, the procedure yields favourable outcomes in terms of safety and IOP reduction. Repeated MP-TLT treatment, if necessary, seems to achieve more sustained IOP reduction than the initial treatment.

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Purpose: We introduce a deep learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). Design: Retrospective analysis of a large data set of retinal OCT images. Participants: A total of 3456 adults aged between 51 and 102 years whose OCT images were collected under the PINNACLE project. Methods: Our system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46 496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct 2 parallel 1.5-hour semistructured interviews with 2 independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome Measures: We checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD, and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late-stage wet and dry AMD against an established clinical grading system and a demographic baseline model. Results: Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognized as known biomarkers used in established grading systems, and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid, and thick from thin choroids, and, in simulation, outperformed clinically used grading systems in prognostic value. Conclusions: Using self-supervised deep learning, we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep learning tools can accelerate the discovery of novel prognostic biomarkers. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues. Firstly, several image transformations which have been shown to be crucial for effective contrastive learning do not translate from the natural image to the medical image domain. Secondly, the assumption made by conventional methods, that any two images are dissimilar, is systematically misleading in medical datasets depicting the same anatomy and disease. This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time. In this paper we tackle these issues by extending conventional contrastive frameworks with a novel metadata-enhanced strategy. Our approach employs widely available patient metadata to approximate the true set of inter-image contrastive relationships. To this end we employ records for patient identity, eye position (i.e. left or right) and time series information. In experiments using two large longitudinal datasets containing 170,427 retinal optical coherence tomography (OCT) images of 7912 patients with age-related macular degeneration (AMD), we evaluate the utility of using metadata to incorporate the temporal dynamics of disease progression into pretraining. Our metadata-enhanced approach outperforms both standard contrastive methods and a retinal image foundation model in five out of six image-level downstream tasks related to AMD. We find benefits in both a low-data and high-data regime across tasks ranging from AMD stage and type classification to prediction of visual acuity. Due to its modularity, our method can be quickly and cost-effectively tested to establish the potential benefits of including available metadata in contrastive pretraining.

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INTRODUCTION: The purpose of this project was to explore the current standards of clinical care genetic testing and counseling for patients with inherited retinal diseases (IRDs) from the perspective of leading experts in selected European countries. Also, to gather opinions on current bottlenecks and future solutions to improve patient care. METHODS: On the initiative of the European Vision Institute, a survey questionnaire with 41 questions was designed and sent to experts in the field from ten European countries. Each participant was asked to answer with reference to the situation in their own country. RESULTS: Sixteen questionnaires were collected by November 2023. IRD genetic tests are performed in clinical care settings for 80% or more of tested patients in 9 countries, and the costs of genetic tests in clinical care are covered by the public health service to the extent of 90% or more in 8 countries. The median proportion of patients who are genetically tested, the median rate of genetically solved patients among those who are tested, and the median proportion of patients receiving counseling are 51-70%, 61-80%, and 61-80%, respectively. Improving the education of healthcare professionals who facilitate patient referrals to specialized centers, improving access of patients to more thorough genotyping, and increasing the number of available counselors were the most advocated solutions. CONCLUSION: There is a significant proportion of IRD patients who are not genetically tested, whose genetic testing is inconclusive, or who do not receive counseling. Educational programs, greater availability of state-of-the-art genotyping and genetic counselors could improve healthcare for IRD patients.

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INTRODUCTION: The aim of this study was to evaluate the progression of atrophy as determined by spectral-domain optical coherence tomography (SD-OCT) in patients with molecularly confirmed ABCA4-associated Stargardt disease type 1 (STGD1) over a 24-month period in a multicenter prospective cohort study. METHODS: SD-OCT images from 428 eyes of 236 patients were analyzed. Change of mean thickness (MT) and intact area were estimated after semiautomated segmentation for the following individual layers in the central subfield (CS), inner ring (IR), and outer ring (OR) of the ETDRS grid: retinal pigment epithelium (RPE), outer segments (OSs), inner segments (IS), outer nuclear layer (ONL) inner retina (IR), and total retina. RESULTS: Statistically significant decreases of all outer retinal layers (RPE, OS, IS, and ONL) could be observed over a 24-month period both in decline of mean retinal thickness and intact area (p < 0.0001, respectively), whereas the IR showed an increase of retinal thickness in the CS and IR and remained unchanged in the OR. CONCLUSIONS: Significant loss could be detected in outer retinal layers by SD-OCT over a 24-month period in patients with STGD1. Loss of thickness and/or intact area of such layers may serve as potential endpoints for clinical trials that aim to slow down the disease progression of STGD1.

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INTRODUCTION: The objective of this study was to evaluate retinal sensitivity in subfields and its association with the novel quantitative contrast sensitivity function (qCSF) in patients with early age-related macular degeneration (eAMD), in patients with intermediate AMD (iAMD), and in healthy controls. METHODS: In this prospective longitudinal study, retinal sensitivity of a customized 24-point grid was assessed by microperimetry Macular Integrity Assessment (MAIA, CenterVue, Padova, Italy) and divided into different subfields. The Multiple Contrast Vision Meter (Adaptive Sensory Technology, San Diego, CA, USA) was used for qCSF testing. Linear models were used to test the association of functional metrics with variables of interest. RESULTS: 92 study eyes from 92 participants were analyzed (13 eAMD, 31 iAMD, and 48 controls). Microperimetry subfield comparison showed significant differences (p < 0.0001) in the control group between superior and inferior hemifield as well as between central and peripheral subfields. For eAMD, significant differences were found between central and peripheral subfields (p < 0.001) and specific subfields (p < 0.05) and finally for iAMD between specific quadrants (p < 0.05) and specific squares (p < 0.05). Significant associations of retinal sensitivity with qCSF metrics were found for the area underneath the logarithmic contrast sensitivity function, contrast acuity and for the contrast sensitivity at specific spatial frequencies. CONCLUSIONS: This study showed significant differences in the evaluated retinal sensitivity subfields, providing localized natural history data for retinal sensitivity in healthy controls and patients with eAMD and iAMD.

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Purpose: Optical coherence tomography (OCT) representations in clinical practice are static and do not allow for a dynamic visualization and quantification of blood flow. This study aims to present a method to analyze retinal blood flow dynamics using time-resolved structural OCT. Methods: We developed novel imaging protocols to acquire video-rate time-resolved OCT B-scans (1024 × 496 pixels, 10 degrees field of view) at four different sensor integration times (integration time of 44.8 µs at a nominal A-scan rate of 20 kHz, 22.4 µs at 40 kHz, 11.2 µs at 85 kHz, and 7.24 µs at 125 kHz). The vessel centers were manually annotated for each B-scan and surrounding subvolumes were extracted. We used a velocity model based on signal-to-noise ratio (SNR) drops due to fringe washout to calculate blood flow velocity profiles in vessels within five optic disc diameters of the optic disc rim. Results: Time-resolved dynamic structural OCT revealed pulsatile SNR changes in the analyzed vessels and allowed the calculation of potential blood flow velocities at all integration times. Fringe washout was stronger in acquisitions with longer integration times; however, the ratio of the average SNR to the peak SNR inside the vessel was similar across all integration times. Conclusions: We demonstrated the feasibility of estimating blood flow profiles based on fringe washout analysis, showing pulsatile dynamics in vessels close to the optic nerve head using structural OCT. Time-resolved dynamic OCT has the potential to uncover valuable blood flow information in clinical settings.

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Pseudoxanthoma elasticum (PXE) is an autosomal-recessively inherited multisystem disease. Mutations in the ABCC6-gene are causative, coding for a transmembrane transporter mainly expressed in hepatocytes, which promotes the efflux of adenosine triphosphate (ATP). This results in low levels of plasma inorganic pyrophosphate (PPi), a critical anti-mineralization factor. The clinical phenotype of PXE is characterized by the effects of elastic fiber calcification in the skin, the cardiovascular system, and the eyes. In the eyes, calcification of Bruch's membrane results in clinically visible lesions, including peau d'orange, angioid streaks, and comet tail lesions. Frequently, patients must be treated for secondary macular neovascularization. No effective therapy is available for treating the cause of PXE, but several promising approaches are emerging. Finding appropriate outcome measures remains a significant challenge for clinical trials in this slowly progressive disease. This review article provides an in-depth summary of the current understanding of PXE and its multi-systemic manifestations. The article offers a detailed overview of the ocular manifestations, including their morphological and functional consequences, as well as potential complications. Lastly, previous and future clinical trials of causative treatments for PXE are discussed.

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The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.779 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.

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Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six-month interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.

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Copy-number variants (CNVs) play a substantial role in the molecular pathogenesis of hereditary disease and cancer, as well as in normal human interindividual variation. However, they are still rather difficult to identify in mainstream sequencing projects, especially involving exome sequencing, because they often occur in DNA regions that are not targeted for analysis. To overcome this problem, we developed OFF-PEAK, a user-friendly CNV detection tool that builds on a denoising approach and the use of "off-target" DNA reads, which are usually discarded by sequencing pipelines. We benchmarked OFF-PEAK on data from targeted sequencing of 96 cancer samples, as well as 130 exomes of individuals with inherited retinal disease from three different populations. For both sets of data, OFF-PEAK demonstrated excellent performance (>95% sensitivity and >80% specificity vs. experimental validation) in detecting CNVs from in silico data alone, indicating its immediate applicability to molecular diagnosis and genetic research.

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Optical coherence tomography angiography (OCTA) enables three-dimensional reconstruction of the functional blood vessels in the retina. Therefore, it enables the quantification of 3D retinal vessel parameters such as surface area and vessel volume. In spite of the widespread use of OCTA, no representative volume-rendered vessel volume (VV) data are published to date. In this study, OCTA 3 × 3 mm macular cubes were processed with volume-rendering techniques to measure VV in 203 eyes from 107 healthy volunteers. Generalized linear models (GLM) were constructed to assess the impact of age, gender, visual acuity (VA), spherical equivalent (SE), and axial length (AL) on VV. Overall mean VV was 0.23 ± 0.05mm3. Age and axial length showed a negative correlation with VV. However, GLM model analysis found that AL exerted the most pronounced influence on VV. No statistically significant associations were identified between gender or between left and right eyes. This is the first study to assess 3D OCTA VV and its naturally occurring variations in a large series of healthy subjects. It offers novel insights into the characterization of normal retinal vascular anatomy in healthy individuals, contributing to a valuable reference for future research in this field.

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PURPOSE: Retinitis pigmentosa GTPase regulator-associated X-linked retinitis pigmentosa ( RPGR -associated XLRP) is a rare and severe form of retinitis pigmentosa, resulting in progressive visual impairment; however, disease progression data are limited. A systematic literature review was conducted to assess available data on disease progression in RPGR -associated XLRP. METHODS: PubMed, Embase, and select congress abstracts were evaluated through June 2022. Eligible studies included results specific to RPGR -associated XLRP or populations with ≥80% of patients with retinitis pigmentosa carrying disease-causing RPGR variants. End points of interest included visual acuity, visual field, ellipsoid zone width, progression to blindness, and patient-reported outcomes. RESULTS: Fourteen studies met ≥1 end point of interest. Progressive declines in visual acuity, visual field, and ellipsoid zone width were reported across studies. Nearly all publications reported annual declines in visual acuity (3.5%-8.2%). Annual visual field declines ranged from 4.2% to 13.3%. Changes in retinal structure were also observed (ellipsoid zone width changes: -177 to -830 µ m/year). Most studies measured blindness using visual acuity; visual field-based definitions resulted in blindness by age ∼25 years. Patient-reported outcome data were limited. CONCLUSION: Published evidence shows that patients with RPGR -associated XLRP experience progressive decline in visual acuity, visual field, and ellipsoid zone width, eventually resulting in blindness. Additional longitudinal data with standardized end points and expanded collection of patient-reported outcomes are needed to assess visual decline in RPGR -associated XLRP.

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There is a need to identify accurately prognostic factors that determine the progression of intermediate to late-stage age-related macular degeneration (AMD). Currently, clinicians cannot provide individualised prognoses of disease progression. Moreover, enriching clinical trials with rapid progressors may facilitate delivery of shorter intervention trials aimed at delaying or preventing progression to late AMD. Thus, we performed a systematic review to outline and assess the accuracy of reporting prognostic factors for the progression of intermediate to late AMD. A meta-analysis was originally planned. Synonyms of AMD and disease progression were used to search Medline and EMBASE for articles investigating AMD progression published between 1991 and 2021. Initial search results included 3229 articles. Predetermined eligibility criteria were employed to systematically screen papers by two reviewers working independently and in duplicate. Quality appraisal and data extraction were performed by a team of reviewers. Only 6 studies met the eligibility criteria. Based on these articles, exploratory prognostic factors for progression of intermediate to late AMD included phenotypic features (e.g. location and size of drusen), age, smoking status, ocular and systemic co-morbidities, race, and genotype. Overall, study heterogeneity precluded reporting by forest plots and meta-analysis. The most commonly reported prognostic factors were baseline drusen volume/size, which was associated with progression to neovascular AMD, and outer retinal thinning linked to progression to geographic atrophy. In conclusion, poor methodological quality of included studies warrants cautious interpretation of our findings. Rigorous studies are warranted to provide robust evidence in the future.

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Supervised deep learning (DL) algorithms are highly dependent on training data for which human graders are assigned, for example, for optical coherence tomography (OCT) image annotation. Despite the tremendous success of DL, due to human judgment, these ground truth labels can be inaccurate and/or ambiguous and cause a human selection bias. We therefore investigated the impact of the size of the ground truth and variable numbers of graders on the predictive performance of the same DL architecture and repeated each experiment three times. The largest training dataset delivered a prediction performance close to that of human experts. All DL systems utilized were highly consistent. Nevertheless, the DL under-performers could not achieve any further autonomous improvement even after repeated training. Furthermore, a quantifiable linear relationship between ground truth ambiguity and the beneficial effect of having a larger amount of ground truth data was detected and marked as the more-ground-truth effect.

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PURPOSE: To evaluate the progression of atrophy as determined by spectral-domain optical coherence tomography (SD-OCT) in patients with molecularly confirmed PROM1-associated retinal degeneration (RD) over a 24-month period. DESIGN: International, multicenter, prospective case series. METHODS: A total of 13 eyes (13 patients) affected with PROM1-associated RD were enrolled at 5 sites and SD-OCT images were obtained at baseline and after 24 months. Loss of mean thickness (MT) and intact area were estimated after semi-automated segmentation for the following individual retinal layers in the central subfield (CS), inner ring, and outer ring of the ETDRS grid: retinal pigment epithelium (RPE), outer segments (OS), inner segments (IS), outer nuclear layer (ONL), inner retina (IR), and total retina (TR). RESULTS: Statistically significant losses of thickness of RPE and TR were detected in the CS and inner ring and of ONL and IS in the outer ring (all P < .05); a statistically significant decrease in the intact area of RPE and IS was observed in the inner ring, and of ONL in the outer ring (all P < .05); the change in MT and the intact area of the other layers showed a trend of decline over an observational period of 24 months. CONCLUSIONS: Significant thickness losses could be detected in outer retinal layers by SD-OCT over a 24-month period in patients with PROM1-associated retinal degeneration. Loss of thickness and/or intact area of such layers may serve as potential endpoints for clinical trials that aim to slow down the disease progression of PROM1-associated retinal degeneration.

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Basement membranes are among the most widespread, non-cellular functional materials in metazoan organisms. Despite this ubiquity, the links between their compositional and biophysical properties are often difficult to establish due to their thin and delicate nature. In this article, we examine these features on a molecular level by combining results from proteomics, elastic, and nanomechanical analyses across a selection of human basement membranes. Comparing results between these different membranes connects certain compositional attributes to distinct nanomechanical signatures and further demonstrates to what extent water defines these properties. In all, these data underline BMs as stiff yet highly elastic connective tissue layers and highlight how the interplay between composition, mechanics and hydration yields such exceptionally adaptable materials.

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Purpose: The purpose of this study was to establish and validate a novel fundus-controlled dark-adaptometry method. Methods: We developed a custom dark-adaptometry software for the S-MAIA device using the open-perimetry-interface. In the validation-substudy, participants underwent dark-adaptometry testing with a comparator device (MonCvONE, 59% rhodopsin bleach, cyan and red stimuli centered at 2 degrees, 4 degrees, and 6 degrees eccentricity). Following a brief break (approximately 5 minutes), the participants were bleached again and underwent dark-adaptometry testing with the S-MAIA device (same loci). In the retest reliability-substudy, participants were tested twice with the S-MAIA device (same loci as above). Nonlinear curve fitting was applied to extract dark-adaptation curve parameters. Validity and repeatability were summarized in terms of the mean bias and 95% limits of agreement (LoAs). Results: In the validation-substudy (N = 20 participants, median age interquartile range [IQR] 31.5 years [IQR = 25.8, 62.0]), measures of rod-mediated dark-adaptation showed little to no between method differences for the cone-rod-break-time (bias 95% confidence interval [95% CI] of +0.1 minutes [95% CI = -0.6 to 0.8]), rod-intercept-time (-0.23 minutes [95% CI = -1.38 to 0.93]), and S2 slope (-0.01 LogUnits/minutes [95% CI = -0.02 to -0.01]). In the retest reliability-substudy (N = 10 participants, 32.0 years [95% CI = 27.0, 57.5]), the corresponding LoAs were (cone-rod-break-time) -3.94 to 2.78 minutes, (rod-intercept-time) -4.55 to 3.11 minutes, and (S2 slope [rate-limited component of rod recovery]) -0.03 to 0.03 LogUnits/minutes. The LoAs for the steady-state cone and rod thresholds were -0.28 to 0.33 LogUnits and -0.34 to 0.28 LogUnits. Conclusions: The devised fundus-controlled dark-adaptometry method yields valid and reliable results. Translational Relevance: Fundus-controlled dark-adaptometry solves the critical need for localized testing of the visual cycle and retinoid transfer in eyes with unstable fixation.

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Purpose: The purpose of this study was to describe, validate, and compare the contrast sensitivity functions (CSFs) acquired with the novel quick CSF (qCSF) method from patients with early and intermediate age-related macular degeneration (eAMD and iAMD) and healthy controls. Methods: This is a cross-sectional analysis of contrast sensitivity (CS) and visual acuity (VA) baseline data from the prospective Multimodal Functional and Structural Visual System Characterization (MUMOVI) study. The qCSF testing was conducted with the manifold contrast vision meter (Adaptive Sensory Technology, San Diego, CA, USA). CS levels at spatial frequencies from 1 cycle per degree (CPD) to 18 CPD, the area underneath the logarithmic contrast sensitivity function (AULCSF), and contrast acuity (CA) were analyzed. The association of functional metrics with variables of interest was tested with linear models. Results: Ninety-four study eyes from 94 study patients were included in the analysis (13 patients with eAMD, 33 patients with iAMD, and 48 healthy controls). Significant differences between the eAMD and the iAMD model estimates were only found for CS at 1 CPD (t value = -2.9, P value = 0.006) and CS at 1.5 CPD (-2.7, 0.01). A specific association between smoking years and CS at 1 CPD (P = 0.02) and CS at 1.5 CPD (P = 0.03) could be described in patients with AMD. Conclusions: The qCSF testing allows the fast measurement of the whole CSF, enabling the integration into clinical routine. We showed that novel qCSF-derived metrics detect slight functional differences between AMD stages, which testing by Pelli-Robson charts or VA testing would miss. This study, therefore, yields novel qCSF-derived candidate metrics for therapeutic trials in AMD.

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INTRODUCTION: The aim of this study was to describe and evaluate double PreserFlo MicroShunt implantation as a modified micro-invasive glaucoma surgery technique and to retrospectively compare the outcomes in a cohort of glaucoma patients with single or double implantation. MATERIALS AND METHODS: A retrospective data analysis of 57 glaucoma patients who consecutively underwent PreserFlo implantation was performed. Medical records were examined for patients' demographics, glaucoma type, intraocular pressure (IOP), medication, complications, and re-interventions. Two groups with single (n = 29) or double (n = 28) implantation were formed, and the outcomes were compared. In cases of two-stage double implantation (n = 17), the courses of the initial and the second implantations were compared. RESULTS: Mean preoperative IOP was significantly higher in the double compared to the single implantation group (29.4 ± 10.0 mm Hg; 21.7 ± 8.2 mm Hg; p = 0.003). Postoperatively, IOP was significantly lower in the double implantation group at various time-points (day 1, week 1, months 3 and 6; all p < 0.021). In the subgroup with two-stage procedures, mean preoperative IOP was 24.5 ± 8.5 mm Hg and 29.8 ± 10.1 mm Hg, respectively (p = 0.128). While immediately postoperatively, mean IOP lowering was clinically significant and similar following both procedures, the longer sustainable effect was observed after the second procedure (month 12: 25.5 ± 7.5 mm Hg; 12.4 ± 4.8 mm Hg; p = 0.001). No serious complications were observed. DISCUSSION/CONCLUSION: Double PreserFlo implantation appears safe and efficient for lowering IOP in glaucoma patients. Our preliminary findings suggest that double is superior to single implantation in terms of IOP lowering and the need for additional topical medication. Patients with insufficient IOP lowering following single implantation may benefit from a second implantation. Further research is warranted to evaluate double implantation as a first-line, one-stage procedure.

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