Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease’s development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.

The surgical management of macular holes is undergoing continuous evolution, with recent focus on the utilization of platelet concentrates as a promising adjunctive intervention. Currently, they present a valid surgical approach for achieving anatomical and functional success with a non-inferiority comparably to the alternative surgical techniques. Nonetheless, the utilization of varied platelet concentrates terminologies, coupled with the lack of standardization in their preparation methodologies, engenders both lexical confusion and challenges in comparing scientific studies published up until now. In this review, we summarized the published evidence concerning the application of platelet concentrates in macular holes surgery, aiming to clarify the terminology and methodologies employed and to establish a common consensus facilitating further development and diffusion of this promising technique.

Macular atrophy is a common complication in neovascular age-related macular degeneration (AMD) and is associated with poorer visual outcomes. This study evaluated inter-reader and inter-modality variability in measuring macular atrophy in previously-treated neovascular AMD eyes without exudation, using six imaging modalities. Prospective, cohort study. Thirty participants with previously-treated neovascular AMD, showing no signs of exudation at the time of enrollment, and exhibiting macular atrophy. During the same clinic visit, patients were imaged using six different imaging modalities: color fundus photography (CFP; Clarus, Carl Zeiss Meditec), near-infrared imaging (NIR; Spectralis; Heidelberg Engineering, Heidelberg, Germany), structural optical coherence tomography (OCT; Spectralis; Heidelberg Engineering, Heidelberg, Germany), green fundus autofluorescence (GAF; Clarus, Carl Zeiss Meditec), blue fundus autofluorescence (BAF; Spectralis; Heidelberg Engineering, Heidelberg, Germany), and pseudocolor imaging (MultiColor; Spectralis; Heidelberg Engineering, Heidelberg, Germany). Two readers independently measured the macular atrophy area. Inter-reader and inter-modality agreement. The 95% coefficient of repeatability (CR) was 5.98 mm² for CFP, 4.46 mm² for MultiColor, 3.90 mm² for BAF, 3.92 mm² for GAF, 4.86 mm² for NIR, and 3.55 mm² for OCT. Similarly, the coefficient of variation (CV) was lowest for the OCT-based grading at 0.08 and highest for the NIR-based grading at 0.28. Accordingly, the intraclass correlation coefficient (ICC) was 0.742 for CFP, 0.805 for MultiColor, 0.857 for BAF, 0.850 for GAF, 0.755 for NIR, and 0.917 for OCT. The 6 different imaging modalities presented measurements with different mean values, with only a limited number of comparisons not significantly different between the instruments, although measurements were correlated. The largest size of macular atrophy was with the structural OCT-based grading (median=4.65 mm²; interquartile range [IQR]=4.78 mm²) and the smallest was with the CFP-based grading (median=3.86 mm²; IQR=5.06 mm²). Inconsistencies arose from various factors. In patients with neovascular AMD, macular atrophy measurements vary significantly depending on the imaging technique used. CFP-based assessments yielded the smallest macular atrophy sizes, while structural OCT-based assessments produced the largest. These discrepancies stem from both the inherent limitations of each modality in assessing RPE atrophy and factors related to neovascularization, such as the coexistence of fibrosis.

Diabetic macular edema (DME)’s therapeutic approach can frequently be challenging. The purpose of the review is to propose evidence-based recommendations on the employment of intravitreal dexamethasone implants (DEX) when approaching patients suffering from DME. Seven national consensuses redacted by different groups of retina specialists from Europe and Asia were examined and confronted. Each consensus was redacted utilizing a Delphi approach, in person meetings, or by reviewing the literature. DEX can be studied as a first-line strategy in individuals suffering from DME with inflammatory OCT biomarkers, in vitrectomized eyes, in patients with recent cardiovascular events, in pregnant women, in patients scheduled to undergo cataract surgery or with poor compliance. The other parameters considered were the indications to the DME treatment, when to switch to DEX, the definition of non-responder to anti-VEGFs agents and to the DEX implant, whether to combine DEX with laser photocoagulation, the association between glaucoma and DEX, and the management of DEX and the cataract. Although several years have passed since the introduction of DEX implants in the DME treatment, there is still not a unified agreement among retina specialists. This paper compares the approach in the DME treatment between countries from different continents and provides a broader and worldwide perspective of the topic.

Background and objectives: Age-related macular degeneration (AMD) is a complex and multifactorial condition that can lead to permanent vision loss once it progresses to the neovascular exudative stage. This review aims to summarize the use of deep learning in neovascular AMD. Materials and Methods: Pubmed search. Results: Deep learning has demonstrated effectiveness in analyzing structural OCT images in patients with neovascular AMD. This review outlines the role of deep learning in identifying and measuring biomarkers linked to an elevated risk of transitioning to the neovascular form of AMD. Additionally, deep learning techniques can quantify critical OCT features associated with neovascular AMD, which have prognostic implications for these patients. Incorporating deep learning into the assessment of neovascular AMD eyes holds promise for enhancing clinical management strategies for affected individuals. Conclusion: Several studies have demonstrated effectiveness of deep learning in assessing neovascular AMD patients and this has a promising role in the assessment of these patients.