PurposeEvaluate whether the axial length of the fellow eye can be used to calculate the intraocular lens (IOL) in eyes with retinal detachment.DesignRetrospective, consecutive case series.MethodsOur study was conducted at the Goethe University and included patients who underwent silicone oil (SO) removal combined with phacoemulsification and IOL implantation. Preoperative examinations included biometry (IOLMaster 700, Carl Zeiss). We measured axial length (AL) of operated eye (OE) or fellow eye (FE) and compared mean prediction error and mean and median absolute prediction error (MedAE) using four formulas and AL of the OE (Barrett Universal II (BUII)-OE). Additionally, we compared the number of eyes within ±0.50, ±1.00 and ±2.00 dioptre (D) from target refraction.ResultsIn total, 77 eyes of 77 patients met our inclusion criteria. MedAE was lowest for the BUII-OE (0.42 D) compared with Kane-FE (1.08 D), BUII-FE (1.02 D) and Radial Basis Function 3.0 (RBF3.0)-FE (1.03 D). This was highly significant (p<0.001). The same accounts for the number of eyes within ±0.50 D of the target refraction with the BUII-OE (44 eyes, 57%) outperforming the RBF3.0-FE (20 eyes, 25.9%), Kane-FE and BUII-FE formula (21 eyes, 27.2%) each.ConclusionOur results show a statistically and clinically highly relevant reduction of IOL power predictability when using the AL of the FE for IOL calculation. Using the AL of the SO filled eye after initial vitrectomy results in significantly better postoperative refractive results. A two-step procedure using the AL of the OE after reattachment of the retina is highly recommended.

Background/aimsWe sought to evaluate widefield swept-source optical coherence tomography angiography (WF SS-OCTA) among eyes with concomitant age-related macular degeneration (AMD) and diabetes mellitus or diabetic retinopathy (DM/DR).MethodsThis cross-sectional, comparative study consisted of three study groups: eyes with (1) AMD and DM/DR, (2) AMD alone and (3) DM/DR alone. WF SS-OCTA (3×3, 6×6 and 12×12 mm) images were captured. Vascular metrics included foveal avascular zone (FAZ), vessel density (VD) and vessel skeletonised density (VSD). Mixed-effects multivariable regression models adjusted for age were performed by cohort and subgroup based on AMD and DR stages.ResultsOur cohort included 287 eyes from 186 patients with an average age of 64±14.0 years old. Results revealed significantly reduced vascular metrics in concomitant AMD and DM/DR eyes (N=68) compared with AMD-only eyes (N=71) on all angiograms but not compared with DM/DR-only eyes (N=148). For example, when compared with AMD-only eyes, AMD and DM/DR eyes had significantly reduced VD (β=−0.03, p=0.016) and VSD (β=−1.09, p=0.022) on 12×12 mm angiograms, increased FAZ perimeter (β=0.51, p=0.025) and FAZ area (β=0.11, p=0.015) on 6×6 mm angiogram, and reductions in all VD and VSD metrics on 3×3 and 6×6 mm angiograms. However, only 3×3 mm angiogram FAZ metrics were significantly different when comparing DM/DR eyes with concomitant AMD and DM/DR eyes.ConclusionWF SS-OCTA revealed significant reductions in retinal microvasculature metrics in AMD and DM/DR eyes compared with AMD-only eyes but not compared with DM/DR-only eyes.

AimWe tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up.Methods3919 eyes (2259 patients) with ≥2 ODPs at least 2 years apart, and ≥5 24–2 VF exams spanning ≥3 years of follow-up were included. Serial VF mean deviation (MD) rates of change were estimated starting at the fifth visit and subsequently by adding visits until final visit. VF progression was defined as a statistically significant negative slope at two consecutive visits and final visit. We built a twin-neural network with ResNet50-backbone. A pair of ODPs acquired up to a year before the VF progression date or the last VF in non-progressing eyes were included as input. Primary outcome measures were area under the receiver operating characteristic curve (AUC) and model accuracy.ResultsThe average (SD) follow-up time and baseline VF MD were 8.1 (4.8) years and –3.3 (4.9) dB, respectively. VF progression was identified in 761 eyes (19%). The median (IQR) time to progression in progressing eyes was 7.3 (4.5–11.1) years. The AUC and accuracy for predicting VF progression were 0.862 (0.812–0.913) and 80.0% (73.9%–84.6%). When only fast-progressing eyes were considered (MD rate < –1.0 dB/year), AUC increased to 0.926 (0.857–0.994).ConclusionsA deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.

BackgroundWe investigated the potential association between pathogenic BRCA1/2 variants and retinoblastoma pathogenicity.MethodsIn this single-centre, retrospective case series, we performed hereditary cancer panel tests using blood samples for patients with retinoblastoma diagnosed between March 2017 and October 2021. Bioinformatics prediction tools were then used to conduct in silico pathogenicity assessments for patients with BRCA1/2 family variants, in addition to the American College of Medical Genetics and Genomics (ACMG) variant classification. One patient with a germline BRCA1 variant was analysed with whole-genome sequencing (WGS), mutational signature analysis and methylation analysis for RB1 and BRCA using the patient’s tumour and blood samples.ResultsOf 30 retinoblastoma patients who underwent panel sequencing, six (20%) were found to carry germline variants in the BRCA1/2 or BRIP1 genes. Among these six patients, two had pathogenic or likely pathogenic variants as per the ACMG variant classification. Additionally, three patients showed potential pathogenic BRCA1/2 family variants through further analysis with alternative bioinformatics prediction tools. In the WGS analysis of a tumour from a patient with a germline likely pathogenic BRCA1 variant in one allele, we observed the loss of one RB1 allele due to a large deletion. No somatic non-synonymous mutations or frameshift indels were detected in the RB1 locus of the remaining allele. This sample also showed BRCA1 gene promoter hypermethylation in the tumour, indicating additional epigenetic silencing.ConclusionThis study demonstrated that some retinoblastoma patients harboured germline BRCA1/2 family variants, which may be associated with the development of retinoblastoma along with RB1 mutations.

BackgroundIt has been reported that the gut microbiome is involved in the pathogenesis of uveitis, but the specific pathogenic microbes and metabolites in different types of uveitis are still unclear.MethodsMicrobiome and metabolites were detected using 16S ribosomal DNA and LC‒MS/MS (liquid chromatography tandem mass spectrometry) in 45 individuals, including 16 patients with Vogt Koyanagi Harada (VKH), 11 patients with acute anterior uveitis (AAU) and 18 healthy controls.ResultThe diversity of intestinal microbes among the VKH, AAU and control groups was not significantly different. Thirteen specific microbes and 38 metabolites were detected in the VKH group, and 7 metabolites (vanillin, erythro-isoleucine, pyrimidine, 1-aminocyclopropanecarboxylic acid, beta-tocopherol, (-)-gallocatechin and N1-methyl-4-pyridone-3-carboxamide) significantly changed only in patients with VKH, which mainly acted on nicotinamide and nicotinamide metabolism and biotin metabolism (p<0.05). Compared with the VKH group, the AAU group had milder intestinal changes. Only 11 specific microbes and 29 metabolites changed in the AAU group, while these metabolites were not specific (p<0.05). These metabolites mainly acted on arachidonic acid metabolism. In addition, three microbes and two metabolites had the same changes in the VKH and AAU groups (p<0.05). Multiple correlations were found between gut microbes and metabolites in the VKH and AAU groups. Six microbes (Pediococcus, Pseudomonas, Rhodococcus, Photobacterium, Gardnerella and Lawsonia) and two metabolites (pyrimidine and gallocatechin) as biomarkers could effectively distinguish patients with VKH from patients with AAU and healthy individuals, with AUC (area under the curve) values greater than 82%. Four microbes (Lentilactobacillus, Lachnospiraceae_UCG-010, Cetobacterium, Liquorilactobacillus) could distinguish patients with AAU from patients with VKH and healthy controls with AUC>76%.ConclusionSignificant differences in intestinal microbes and metabolites suggest their different roles in the pathogenesis of uveitis entities. Changes in the metabolism of certain B vitamins may be involved in the pathogenesis of VKH.

AimTo perform an independent validation of deep learning (DL) algorithms for automated scleral spur detection and measurement of scleral spur-based biometric parameters in anterior segment optical coherence tomography (AS-OCT) images.MethodsPatients receiving routine eye care underwent AS-OCT imaging using the ANTERION OCT system (Heidelberg Engineering, Heidelberg, Germany). Scleral spur locations were marked by three human graders (reference, expert and novice) and predicted using DL algorithms developed by Heidelberg Engineering that prioritise a false positive rate <4% (FPR4) or true positive rate >95% (TPR95). Performance of human graders and DL algorithms were evaluated based on agreement of scleral spur locations and biometric measurements with the reference grader.Results1308 AS-OCT images were obtained from 117 participants. Median differences in scleral spur locations from reference locations were significantly smaller (p<0.001) for the FPR4 (52.6±48.6 µm) and TPR95 (55.5±50.6 µm) algorithms compared with the expert (61.1±65.7 µm) and novice (79.4±74.9 µm) graders. Intergrader reproducibility of biometric measurements was excellent overall for all four (intraclass correlation coefficient range 0.918–0.997). Intergrader reproducibility of the expert grader (0.567–0.965) and DL algorithms (0.746–0.979) exceeded that of the novice grader (0.146–0.929) for images with narrow angles defined by OCT measurement of angle opening distance 500 µm anterior to the scleral spur (AOD500)<150 µm.ConclusionsDL algorithms on the ANTERION approximate expert-level measurement of scleral spur-based biometric parameters in an independent patient population. These algorithms could enhance clinical utility of AS-OCT imaging, especially for evaluating patients with angle closure and performing intraocular lens calculations.

Background/aimsTo identify ocular determinants of iridolenticular contact area (ILCA), a recently introduced swept-source optical coherence tomography (SSOCT) derived parameter, and assess the association between ILCA and angle closure.MethodsIn this population-based cross-sectional study, right eyes of 464 subjects underwent SSOCT (SS-1000, CASIA, Tomey Corporation, Nagoya, Japan) imaging in the dark. Eight out of 128 cross-sectional images (evenly spaced 22.5° apart) were selected for analysis. Matlab (Matworks, Massachusetts, USA) was used to measure ILCA, defined as the circumferential extent of contact area between the pigmented iris epithelium and anterior lens surface. Gonioscopic angle closure (GAC) was defined as non-visibility of the posterior trabecular meshwork in two or more angle quadrants.ResultsThe mean age of subjects was 62±6.6 years, with the majority being female (65.5%). 143/464 subjects (28.6%) had GAC. In multivariable linear regression analysis, ILCA was significantly associated with anterior chamber width (β=1.03, p=0.003), pupillary diameter (β=−1.9, p<0.001) and iris curvature (β=−17.35, p<0.001). ILCA was smaller in eyes with GAC compared with those with open angles (4.28±1.6 mm2 vs 6.02±2.71 mm2, p<0.001). ILCA was independently associated with GAC (β=−0.03, p<0.001), iridotrabecular contact index (β=−6.82, p<0.001) or angle opening distance (β=0.02, p<0.001) after adjusting for covariates. The diagnostic performance of ILCA for detecting GAC was acceptable (AUC=0.69).ConclusionsILCA is a significant predictor of angle closure independent of other biometric factors and may reflect unique anatomical information associated with pupillary block. ILCA represents a novel biometric risk factor in eyes with angle closure.

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/aimsTrachomatous trichiasis (TT) is a severe consequence of chronic inflammation/conjunctival scarring resulting from trachoma, the leading infectious cause of blindness worldwide. Our prospective cohort study evaluated the effectiveness of refresher training (RT) for experienced surgeons (1–22 years) on the outcomes of upper lid (UL) TT surgery in rural Ethiopia.MethodsPatients undergoing UL TT surgery in at least one eye by a participating surgeon were included. Patients were split into two cohorts: patients enrolled prior to (C1) and after (C2) RT. RT consisted of a 1-week programme with practice on a HEAD START mannequin and supportive supervision in live surgery by expert trainers. Data were collected at preoperative enrolment, and at 6-month and 12-month follow-up visits. The primary outcome was development of postoperative TT (PTT). A series of multivariate generalised estimating equations were fit to model PTT involving potential covariates of interest.ResultsA total of 261 eyes contributed by 173 patients were studied between 2017 and 2019. By 1-year postoperatively, 37/128 eyes (28.9%) in C1 and 22/133 eyes (16.5%) in C2 had developed PTT (p=0.03). Other than surgeon RT participation, no factors studied were associated with differences in PTT.ConclusionOur results indicate a significant reduction in the risk of PTT after experienced surgeons’ participation in RT as compared with eyes receiving surgery before RT. This observation suggests a significant potential benefit of the RT with HEAD START mannequin practice and supportive supervision during surgery, and suggests RT may be a valuable strategy to improve surgical outcomes.

MicroRNAs belong to the family of non-coding RNAs that participate in cell proliferation, cell death and development. The Müller glial cells are the inherent and specific neuroglia cells in the retinal organisation and play significant roles in retinal neuroprotection, organisational maintenance, inflammation and immunity, regeneration, and the occurrence and development of retinal diseases. However, only a few studies report the underlying mechanism of how miRNAs drive the function of Müller glial cells in the development of retinal diseases. This review aims to summarise the roles of miRNAs in retinal Müller glial cell function, including gliogenesis, inflammation and immunity, regeneration, the development of retinal diseases, and retinal development. This review may point out a novel miRNA-based insight into retinal repair and regeneration. MiRNAs in Müller glial cells may be considered a diagnostic and therapeutic target in the process of retinal repair and regeneration.