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: 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.

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.

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.

Thyroid Eye Disease (TED) is a debilitating autoimmune condition often associated with thyroid dysfunction, leading to significant ocular and orbital morbidity. This review explores recent advancements in the management of TED, focusing on both medical and surgical innovations. The introduction of Teprotumumab, the first FDA-approved drug specifically for TED, marks a pivotal development in medical therapy. Teprotumumab targets the insulin-like growth factor-1 receptor (IGF-1R), effectively reducing inflammation and tissue remodeling. Clinical trials demonstrate its efficacy in reducing proptosis and improving quality of life, making it a cornerstone in the treatment of active, moderate-to-severe TED. Surgical management remains critical for patients with chronic TED or those unresponsive to medical therapy. Advancements in orbital decompression surgery, including image-guided and minimally invasive techniques, offer improved outcomes and reduced complications. Innovations in eyelid and strabismus surgery enhance functional and cosmetic results, further improving patient satisfaction. The management of TED necessitates a multidisciplinary approach involving endocrinologists, ophthalmologists, oculoplastic surgeons, radiologists, and other specialists. This collaborative strategy ensures comprehensive care, addressing the diverse aspects of TED from thyroid dysfunction to ocular health and psychological well-being. Future directions in TED treatment include emerging pharmacological therapies targeting different aspects of the disease’s pathophysiology and advanced surgical techniques aimed at enhancing precision and safety. This review underscores the importance of a personalized, multidisciplinary approach in managing TED, highlighting current advancements, and exploring potential future innovations to improve patient outcomes and quality of life.