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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. Aim: This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). Methods: The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. Results: Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. Conclusion: A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases. [ABSTRACT FROM AUTHOR]

Introduction: We report a case and discuss the clinical characteristics and treatment of spontaneous Descemet membrane detachment (DMD). Case description: We describe a rare case of spontaneous DMD in a patient with prior anterior uveitis and provide a review of the current literature. A 20-year-old woman with a prior history of anterior uveitis presented with vision loss in the left eye. The slit-lamp examination showed corneal edema secondary to DMD, confirmed by anterior segment optical coherence tomography (AS-OCT). The patient underwent an intracameral injection of 20% sulphur hexafluoride (SF6) with complete resolution of the DMD. Although rare, several cases of spontaneous DMD have been reported in the literature, mostly occurring after intraocular surgery. We searched the Pubmed database (1949–2021) for peer-reviewed publications relevant to the topic of spontaneous DMD. Discussion: The pathogenesis of spontaneous DMD is complex and depends on several factors. It can occur due to anatomical anomalies, inflammatory disease, trauma, chemical injuries, and surgical or laser procedures. In most cases, early diagnosis and appropriate management led to resolution. [ABSTRACT FROM AUTHOR]