Background/Objectives: Nailfold videocapillaroscopy (NVC) is a non-invasive method for visualizing systemic micro-circulation, primarily used in rheumatology. Many ocular diseases (e.g., glaucoma, diabetic retinopathy (DR), and central serous chorioretinopathy (CSC)) involve microvascular disturbances. Since microangiopathies are often systemic, NVC findings may reflect ocular pathology. This narrative review aimed to summarize current evidence linking NVC alterations with retinal, choroidal, and optic nerve diseases. Methods: A literature search of PubMed, Scopus, and Web of Science (2000-2025) was conducted using the keywords "nailfold videocapillaroscopy," "ocular diseases," "retinopathy," and "glaucoma". Results: Most available studies were cross-sectional and exploratory. In glaucoma, NVC abnormalities suggesting systemic hypoperfusion (reduced capillary density, avascular areas, tortuosity, and microhemorrhages) were frequently reported. CSC was associated with capillary dilation patterns (megacapillaries and aneurysmal dilations), supporting a congestive rather than ischemic microvascular profile. In DR, NVC abnormalities (reduced density and neoangiogenesis) correlated with DR severity. Associations were also found for AMD and idiopathic macular telangiectasia type 2 (MacTel2, also known as IMT). However, only a limited number of prospective studies assessed diagnostic performance, and data on sensitivity, specificity, or ROC-based validation remain scarce. Conclusions: Current evidence suggests that NVC reflects systemic microvascular alterations associated with several ocular diseases. While NVC shows potential as an adjunctive tool for risk assessment and phenotyping, its diagnostic validity has not yet been established. Limitations include the predominantly observational nature of the studies, heterogeneity of methodologies, and the lack of standardized diagnostic thresholds. Prospective trials integrating NVC with ocular imaging modalities, such as OCT angiography, are needed to determine its clinical utility.

Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

The intraretinal hyperreflective line (IHL) is a novel posterior segment finding demonstrable using careful optical coherence tomography (OCT) examination. It likely indicates a reaction against photoreceptor, Muller cell, and/or retinal pigment epithelial damage. This study analyzed the spectral-domain OCT characteristics of IHLs to disclose their presence in various retinal conditions. A retrospective review of the charted and imaging records of participants with IHL was conducted at Dokuz Eylul University Department of Ophthalmology between January 2019 and August 2023. The inclusion criterion was the detection of an IHL on good-quality B-scan spectral-domain OCT. An IHL was defined as a vertical line extending from the ellipsoid zone band (or lower) through the outer nuclear layer to the internal limiting membrane in the central fovea. Associated retinal conditions were recorded as potential causative factors for the presence of IHL. IHL was observed on spectral-domain OCT in 40 eyes of 38 participants with several retinal diseases assessment. Fourteen eyes (35%) underwent vitreoretinal surgery pre-IHL detection (12 were operated for full-thickness macular hole [FTMH], one for epiretinal membrane [ERM], and one for rhegmatogenous retinal detachment). In six eyes (15%) a microhole coexisted. Four eyes (10%) had a concurrent lamellar macular hole. The IHL preceded the occurrence of FTMH in three eyes (7.5%), and diabetic macular edema and type 2 idiopathic macular telangiectasia (MacTel-2) were present in three eyes (7.5%) each. The remaining conditions included vitreomacular traction (VMT), nonarteritic anterior ischemic optic neuropathy with central retinal artery occlusion, commotio retinae, exudative age-related macular degeneration, ERM, non-infectious idiopathic posterior uveitis, and Coats' disease, each affecting one eye (2.5%). Of the two participants with bilateral involvement, one was diagnosed with MacTel-2 and the other had IHL with VMT in the right eye that was detected post-vitreoretinal surgery for FTMH in the left eye. Although IHLs are mostly identified in eyes with vitreomacular surface diseases, clinicians may encounter IHLs in other types of retinal pathology. Further large-scale, multicenter, long-term studies on the presence of IHLs in OCT imaging are required to provide more substantial insight on this biomarker.

We aimed to evaluate the anatomical and functional outcome of selective photocoagulation of idiopathic macular telangiectasia type 1 by navigated focal laser (Navilas, OD-OS GmBH). Consecutive patients with idiopathic macular telangiectasia type 1 were included in the analysis. All patients were treated with navigated focal laser, planned on multimodal imaging. Seven eyes of seven patients were retrospectively analyzed. Navigated laser photocoagulation of idiopathic macular telangiectasia type 1 successfully occluded the microaneurysms, inducing regression of macular edema and exudation, significative improvement in best-corrected visual acuity at 3 (P = 0.035) and 6 months (P = 0.034) and a decrease in central macular thickness at 3 (P = 0.01) and 6 months (P = 0.01). Patients with idiopathic macular telangiectasia type 1 are ideal candidates for navigated laser treatment. Navigated focal treatment has been shown to be effective and safe in occluding aneurysmal dilations without any side effects. [Ophthalmic Surg Lasers Imaging Retina 2024;55:545-551.].

Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP). We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale. A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants. We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale. Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP. The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values. Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.