The ability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2′s) to cause multi-organ ischemia and coronavirus-induced posterior segment eye diseases in mammals gave concern about potential sight-threatening ischemia in post coronavirus disease 2019 patients. The radial peripapillary capillary plexus (RPCP) is a sensitive target due to the important role in the vascular supply of the peripapillary retinal nerve fiber layer (RNFL). Eighty patients one month after SARS-CoV-2 infection and 30 healthy patients were selected to undergo structural OCT (optical coherence tomography) and OCTA (optical coherence tomography angiography) exams. Primary outcome was a difference in RPCP perfusion density (RPCP-PD) and RPCP flow index (RPCP-FI). No significant difference was observed in age, sex, intraocular pressure (IOP) and prevalence of myopia. RPCP-PD was lower in post SARS-CoV-2 patients compared to controls. Within the post-COVID-19 group, patients with systemic arterial hypertension had lower RPCP-FI and age was inversely correlated to both RPCP-FI and RPCP-PD. Patients treated with lopinavir + ritonavir or antiplatelet therapy during admission had lower RPCP-FI and RPCP-PD. RNFL average thickness was linearly correlated to RPCP-FI and RPCP-PD within post-COVID-19 group. Future studies will be needed to address the hypothesis of a microvascular retinal impairment in individuals who recovered from SARS-CoV-2 infection.

Background: If left untreated, glaucoma—the second most common cause of blindness worldwide—causes irreversible visual loss due to a gradual neurodegeneration of the retinal ganglion cells. Conventional techniques for identifying glaucoma, like optical coherence tomography (OCT) and visual field exams, are frequently laborious and dependent on subjective interpretation. Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. Aims: The purpose of this study is to examine the current uses of AI in the early diagnosis, treatment, and detection of glaucoma while highlighting the advantages and drawbacks of different AI models and algorithms. In addition, it aims to determine how AI technologies might transform glaucoma treatment and suggest future lines of inquiry for this area of study. Methods: A thorough search of databases, including Web of Science, PubMed, and Scopus, was carried out to find pertinent papers released until August 2024. The inclusion criteria were limited to research published in English in peer-reviewed publications that used AI, ML, or DL to diagnose or treat glaucoma in human subjects. Articles were chosen and vetted according to their quality, contribution to the field, and relevancy. Results: Convolutional neural networks (CNNs) and other deep learning algorithms are among the AI models included in this paper that have been shown to have excellent sensitivity and specificity in identifying glaucomatous alterations in fundus photos, OCT scans, and visual field tests. By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. Nonetheless, several significant obstacles remain, such as the requirement for various training datasets, outside validation, decision-making transparency, and handling moral and legal issues. Conclusions: Artificial intelligence (AI) holds great promise for improving the diagnosis and treatment of glaucoma by facilitating prompt and precise interpretation of imaging data and assisting in clinical decision making. To guarantee wider accessibility and better patient results, future research should create strong generalizable AI models validated in various populations, address ethical and legal matters, and incorporate AI into clinical practice.

Background/Objectives: Although it comprises only 5% of all melanomas, uveal melanoma (UM) is the most commonly observed primary intraocular cancer. Methods: Poor patient survival persists in spite of innovative systemic therapies. In fact, approximately fifty percent of UM patients develop metastases from micro-metastases that remain undetected at the exact time of diagnosis. Results: The molecular understanding of UM is significantly enhanced by the recent identification of several mutations that are responsible for the metastasis, growth, and survival of UM. The crucial point is a more accurate genetic analysis for patient follow-up and metastatic risk prediction. Conclusions: This review provides a brief summary of the molecular features of UM that are recently discovered, as well as cytogenetic markers and biochemical pathways that are associated with the development of UM metastases.

The treatment of retinal detachment (RD) has seen numerous advancements in the last decades. Scleral buckling (SB) is a surgical procedure introduced in the 1950s that has seen a drastic reduction with the advent of vitrectomy. However, due to the new surgical visualization systems, SB has evolved and continues to be an extremely useful procedure in certain conditions. The presence of different case reports or interventional studies with comparable outcomes, as well as the lack of recent studies with direct comparison, may result in an underestimation of its potential nowadays. The aim of this review is to provide a comprehensive update on chandelier-assisted scleral bucking (CSB), with an overview of the surgical evolution, outcomes, advantages, and complications.

Proliferative vitreoretinopathy (PVR) has traditionally been managed with vitreoretinal surgery. Although there have been several recent innovations in this surgery to make the retinal approach as uninvasive as possible, the outcomes remain unsatisfactory. Significant complications remain and the complexity of the surgical approach is challenging. The focus of this review was to investigate and discuss the effectiveness of nanomedicine, featuring a wide range of drugs and molecules, as a novel potential treatment for PVR. To date, ocular drug delivery remains a significant issue due to the physiological and anatomical barriers, dynamic or static, which prevent the entry of exogenous molecules. We tried to summarize the nanotechnology-based ophthalmic drugs and new nanoparticles currently under research, with the intention of tackling the onset and development of PVR. The purpose of this review was to thoroughly and analytically examine and assess the potential of nano-based techniques as innovative strategies to treat proliferative vitreoretinopathy (PVR). This study aimed to emphasize the breakthroughs in nanomedicine that provide promising therapeutic options to enhance the results of vitreoretinal surgery and halt disease progression, considering the complexity and difficulty of PVR treatment. The future directions of the nanoparticles and nanotherapies applied to PVR highlight the importance of investing in the development of better designs and novel ophthalmic formulations in order to accomplish a mini-invasive ocular approach, replacing the standard-of-care vitreoretinal surgery.

Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. AI is particularly important for remote or isolated communities, as it allows patients to endure tests and receive rapid initial diagnoses without the necessity of extensive travel and long wait times for medical consultations. Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. The automatic detection of retinal changes might be optimized by AI, allowing one to choose the most effective treatment for nAMD. The complex retinal tissue is well-suited for scanning and easily accessible by modern AI-assisted multi-imaging techniques. AI enables us to enhance patient management by effectively evaluating extensive data, facilitating timely diagnosis and long-term prognosis. Novel applications of AI to nAMD have focused on image analysis, specifically for the automated segmentation, extraction, and quantification of imaging-based features included within optical coherence tomography (OCT) pictures. To date, we cannot state that AI could accurately forecast the therapy that would be necessary for a single patient to achieve the best visual outcome. A small number of large datasets with high-quality OCT, lack of data about alternative treatment strategies, and absence of OCT standards are the challenges for the development of AI models for nAMD.