ObjectiveTo investigate whether there is a causal relationship between generalized anxiety disorder (GAD) and myopia.DesignA bidirectional 2-sample Mendelian randomization (MR) analysis.SubjectsFor the forward MR, 11 single nucleotide polymorphisms (SNPs) associated with GAD were obtained from the FinnGen Research (5280 cases and 368 054 controls; European). For reverse MR, 253 SNPs associated with mean spherical equivalent (MSE) were derived from the UK Biobank (95 619 participants; European).MethodsSummary-level data from the 2 genome-wide association studies were used to conduct bidirectional MR analyses. Four MR methodologies—inverse variance weighted (IVW), MR-Egger regression, weighted median, and weighted mode—were employed, with IVW serving as the primary analysis. Sensitivity analyses, including MR-Egger intercept and heterogeneity Q test, were performed.Main Outcome MeasuresThe outcomes of the forward and reverse MR analyses were MSE and GAD, respectively.ResultsThis study demonstrated a significant causal effect of GAD and myopia, suggesting individuals with GAD are at an elevated risk of more myopic MSE (IVW: β = −0.138; 95% confidence interval [CI] −0.221 to −0.056, P = 0.001). Conversely, we did not find evidence to support a causal effect of myopia on GAD (IVW: β = −0.017; 95% CI −0. 051 to −0.016, P = 0.308). Sensitivity analyses revealed no evidence of genetic confounding to bias the estimate of causal effect.ConclusionsOur study demonstrates a significant causal relationship between GAD and a more myopic MSE, though the effect size was relatively small. These findings could be taken into consideration while implementing mental health interventions in children.Financial Disclosure(s)Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

To explore the correlation between radiomics features extracted from OCT angiography (OCTA) of epiretinal membranes (ERMs) and baseline best-corrected visual acuity (BCVA). Retrospective observational monocentric study. Eighty-three eyes affected by idiopathic ERMs, categorized into low (≤70 letters) and high (70 letters) BCVA groups. The central 3 × 3 mm2 crop of structural and vascular en-face OCTA scans of superficial and deep retina slab, and choriocapillaris of each eye was selected. PyRadiomics was used to extract 86 features belonging to 2 different families: intensity-based statistical features describing the gray-level distribution, and textural features capturing the spatial arrangement of pixels. By employing a greedy strategy, 4 radiomic features were selected to build the final logistic regression model. The ability of the model to discriminate between low and high baseline BCVA was quantified in terms of area under the receiver operating characteristics curve (AUC). The 4 selected informative radiomic features were as follows: the difference average (glcm_DifferenceAverage), quantifying the average difference in gray-level between neighboring pixels; the informational measure of correlation (glcm_Imc1), giving information about the spatial correlation of pixel intensities inside the image; the long run low gray-level emphasis (glrlm_LongRunLowGrayLevelEmphasis), highlighting long segments of low gray-level values within the image; and the large area emphasis (glszm_LargeAreaEmphasis), which quantifies the tendency for larger zones of uniform intensity to occur. No features exhibited a statistically significant difference between low and high BCVA values for the superficial and deep retinal slabs. Conversely, in the choriocapillaris layer, the glcm_DifferenceAverage and glcm_Imc1 features were significantly higher in the high BCVA group (P = 0.047), whereas higher values for the glrlm_LongRunLowGrayLevelEmphasis and glszm_LargeAreaEmphasis were associated with the low BCVA group (P = 0.047). Overall, these radiomic features predicted BCVA with an AUC (95% confidence interval) of 0.74 (0.63-0.85) and sensitivity/specificity of 0.67/0.75. During the cross-validation, the metrics remained stable. Radiomics features of the choriocapillaris in idiopathic ERMs showed a correlation with BCVA, with lower structural complexity and higher homogeneity, together with the presence of homogeneous areas with low-intensity pixel values, reflecting flow voids due to reduced microvascular perfusion, and were correlated with lower visual acuity. The author(s) have no proprietary or commercial interest in any materials discussed in this article.

PurposeSegmentations of retinal layers in spectral-domain optical coherence tomography (SD-OCT) images serve as a crucial tool for identifying and analyzing the progression of various retinal diseases, encompassing a broad spectrum of abnormalities associated with age-related macular degeneration (AMD). The training of deep-learning algorithms necessitates well-defined ground-truth labels, validated by experts, to delineate boundaries accurately. However, this resource-intensive process has constrained the widespread application of such algorithms across diverse OCT devices. This work validates deep learning image segmentation models across multiple OCT devices by testing robustness in generating clinically relevant metrics. DesignProspective, comparative study. ParticipantsAdults over 50 years of age with no AMD to advanced AMD, as defined in the Age-Related Eye Disease Study (AREDS)), in at least one eye, were enrolled. 402 SD-OCT scans were used in this study. MethodsWe evaluate two separate state-of-the-art segmentation algorithms through a training process using images obtained from one OCT device (Heidelberg-Spectralis) and subsequent testing using images acquired from two OCT devices (Heidelberg-Spectralis and Zeiss-Cirrus). This assessment is performed on a dataset that encompasses a range of retinal pathologies, spanning from disease-free conditions to severe forms of AMD, with a focus on evaluating the device independence of the algorithms. Main Outcome MeasuresPerformance metrics (mean-squared-error, mean-absolute-error, dice-coefficients) for the segmentations of the internal limiting membrane (ILM), retinal-pigment-epithelium (RPE), and RPE to Bruch’s-membrane (BM) region, along with en face thickness maps, volumetric estimations (in mm3). Violin plots and Bland-Altman plots comparing predictions against ground-truth are also presented. ResultsThe UNet and DeepLabv3, trained on Spectralis B-scans, demonstrate clinically useful outcomes when applied to Cirrus test B-scans. Review of the Cirrus test-data by two independent annotators revealed that the aggregated-mean-absolute-error in pixels for ILM was 1.82±0.24 (equivalent to 7.0±0.9 μm) and for RPE was 2.46±0.66 (9.5±2.6 μm). Additionally, the dice-similarity-coefficient for the RPE-drusen complex (RPE-DC) region, comparing predictions to ground truth, reached 0.87±0.01. ConclusionIn the pursuit of task-specific goals such as retinal layer segmentation, a segmentation network has the capacity to acquire domain-independent features from a large training dataset. This enables the utilization of the network to execute tasks in domains where ground truth is hard to generate.

PurposeTo compare the effect of faricimab, a dual angiopoietin-2 (Ang-2) and VEGF-A inhibitor, with aflibercept on resolution of hyperreflective foci (HRF) in patients with diabetic macular edema (DME).DesignA post hoc analysis of the randomized, double-masked, noninferiority YOSEMITE/RHINE (NCT03622580/NCT03622593) phase III trials.ParticipantsAdults with vision loss due to center-involving DME.MethodsA deep learning–based algorithm was used to automatically quantify HRF in spectral-domain OCT volume scans from YOSEMITE/RHINE. Study eyes were randomized to faricimab 6.0 mg every 8 weeks (Q8W; n = 519), faricimab 6.0 mg according to a personalized treat-and-extend (T&E)–based regimen (n = 524), and aflibercept 2.0 mg Q8W (n = 502). Hyperreflective foci were defined as hyperreflective objects up to 50 μm in diameter and assessed within the 1.0-mm and 3.0-mm–diameter ETDRS rings and by location within the inner and outer retina.Main Outcome MeasuresHyperreflective foci volume and count at baseline and over time through week 48 in the inner, outer, and total retina, 1-mm and 3-mm diameters; time to absence of HRF at 2 consecutive visits in the inner and outer retina, 1-mm diameter over 48 weeks.ResultsAdjusted mean HRF volumes at week 48 were lower for faricimab Q8W (104.1 picoliter [pL]) and faricimab T&E (110.1 pL) compared with aflibercept (180.3 pL; nominal P < 0.001 for both) in the inner retina, 1-mm diameter. In the inner retina, 3-mm diameter adjusted mean HRF volumes at week 48 were lower for faricimab Q8W (763.9 pL) and faricimab T&E (777.2 pL) compared with aflibercept (1030.6 pL; nominal P < 0.001 for both). Similar results were obtained for volumes in the outer retina and for HRF counts. In the inner retina, 1-mm diameter, the 25th percentile for time to absence of HRF count at 2 consecutive visits was achieved 8 weeks earlier with faricimab Q8W and faricimab T&E versus aflibercept.ConclusionsGreater HRF reductions were achieved with faricimab versus aflibercept, supporting the therapeutic potential of dual Ang-2/VEGF-A inhibition to suppress disease activity in DME.Financial Disclosure(s)Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.