Medical imaging is the mainstay of clinical diagnosis and management. Optical coherence tomography (OCT) is a non-invasive imaging technology that has revolutionized the field of ophthalmology. Since its introduction, OCT has undergone significant improvements in image quality, speed, and resolution, making it an essential diagnostic tool for various ocular pathologies. OCT has not only improved the diagnosis and management of ocular diseases but has also found applications in other fields of medicine. In this manuscript, we provide a brief overview of the history of OCT, its current uses and diagnostic capabilities to assess the posterior segment of the eye, and the evolution of this technology from time-domain (TD) to spectral-domain (SD) and swept-source (SS). This brief review will also discuss the limitations, advantages, disadvantages, and future perspectives of this technology in the field of ophthalmology.

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the “black box phenomenon”, biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.

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.

Background: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions. Aim: This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions. Methods: A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included. Results: The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care. Conclusions: Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.

Diabetic macular edema (DME) is a common complication of diabetes mellitus and a leading cause of visual impairment worldwide. It is defined as the diabetes-related accumulation of fluid, proteins, and lipids, with retinal thickening, within the macular area. DME affects a significant proportion of individuals with diabetes, with the prevalence increasing with disease duration and severity. It is estimated that approximately 25–30% of diabetic patients will develop DME during their lifetime. Poor glycemic control, hypertension, hyperlipidemia, diabetes duration, and genetic predisposition are recognized as risk factors for the development and progression of DME. Although the exact pathophysiology is still not completely understood, it has been demonstrated that chronic hyperglycemia triggers a cascade of biochemical processes, including increased oxidative stress, inflammation, activation of vascular endothelial growth factor (VEGF), cellular dysfunction, and apoptosis, with breakdown of the blood-retinal barriers and fluid accumulation within the macular area. Early diagnosis and appropriate management of DME are crucial for improving visual outcomes. Although the control of systemic risk factors still remains the most important strategy in DME treatment, intravitreal pharmacotherapy with anti-VEGF molecules or steroids is currently considered the first-line approach in DME patients, whereas macular laser photocoagulation and pars plana vitrectomy may be useful in selected cases. Available intravitreal steroids, including triamcinolone acetonide injections and dexamethasone and fluocinolone acetonide implants, exert their therapeutic effect by reducing inflammation, inhibiting VEGF expression, stabilizing the blood-retinal barrier and thus reducing vascular permeability. They have been demonstrated to be effective in reducing macular edema and improving visual outcomes in DME patients but are associated with a high risk of intraocular pressure elevation and cataract development, so their use requires an accurate patient selection. This manuscript aims to provide a comprehensive overview of the pathology, epidemiology, risk factors, physiopathology, clinical features, treatment mechanisms of actions, treatment options, prognosis, and ongoing clinical studies related to the treatment of DME, with particular consideration of intravitreal steroids therapy.

The complicated neurological syndrome known as multiple sclerosis (MS) is typified by demyelination, inflammation, and neurodegeneration in the central nervous system (CNS). Managing this crippling illness requires an understanding of the complex interactions between neurophysiological systems, diagnostic techniques, and therapeutic methods. A complex series of processes, including immunological dysregulation, inflammation, and neurodegeneration, are involved in the pathogenesis of MS. Gene predisposition, autoreactive T cells, B cells, and cytokines are essential participants in the development of the disease. Demyelination interferes with the ability of the CNS to transmit signals, which can cause a variety of neurological symptoms, including impaired motor function, sensory deficiencies, and cognitive decline. Developing tailored therapeutics requires understanding the underlying processes guiding the course of the disease. Neuroimaging, laboratory testing, and clinical examination are all necessary for an accurate MS diagnosis. Evoked potentials and cerebrospinal fluid studies assist in verifying the diagnosis, but magnetic resonance imaging (MRI) is essential for identifying distinctive lesions in the CNS. Novel biomarkers have the potential to increase diagnostic precision and forecast prognosis. The goals of MS treatment options are to control symptoms, lower disease activity, and enhance quality of life. To stop relapses and reduce the course of the disease, disease-modifying treatments (DMTs) target several components of the immune response. DMTs that are now on the market include interferons, glatiramer acetate, monoclonal antibodies, and oral immunomodulators; each has a unique mode of action and safety profile. Symptomatic treatments improve patients' general well-being by addressing specific symptoms, including pain, sphincter disorders, fatigue, and spasticity. Novel treatment targets, neuroprotective tactics, and personalized medicine techniques will be the main focus of MS research in the future. Improving long-term outcomes for MS patients and optimizing disease treatment may be possible by utilizing immunology, genetics, and neuroimaging developments. This study concludes by highlighting the complexity of multiple MS, including its changing therapeutic landscape, diagnostic problems, and neurophysiological foundations. A thorough grasp of these elements is essential to improving our capacity to identify, manage, and eventually overcome this intricate neurological condition.