Survey of ophthalmology最新文献

Visible light serves as a crucial medium for vision formation.;however, prolonged or excessive exposure to light is recognized as a significant etiological factor contributing to retinal degenerative diseases. The retina, with its unique structure and adaptability, relies on the homeostasis of cellular functions to maintain visual health. Under normal conditions, the retina can mount adaptive responses to various insults, including light-induced damage. Unfortunately, exposure to intense and excessive light triggers a cascade of pathological alterations in retinal photoreceptor cells, pigment epithelial cells, ganglion cells, and glial cells. These alterations encompass disruption of intracellular REDOX and Ca²⁺ homeostasis, pyroptosis, endoplasmic reticulum stress, autophagy, and the release of inflammatory cytokines, culminating in irreversible retinal damage. We first delineate the mechanisms of retinal light damage through 4 main avenues: mitochondria function, endoplasmic reticulum stress, cell autophagy, and inflammation. Subsequently, we discuss protective strategies against retinal light damage, aiming to guide research toward the prevention and treatment of light-induced retinal conditions.

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Conventional manual evaluation techniques for MGD face limitations characterized by inefficiencies, high subjectivity, limited big data processing capabilities, and a dearth of quantitative analytical tools. With rapidly advancing artificial intelligence (AI) techniques revolutionizing ophthalmology, studies are now leveraging sophisticated AI methodologies--including computer vision, unsupervised learning, and supervised learning--to facilitate comprehensive analyses of meibomian gland (MG) evaluations. These evaluations employ various techniques, including slit lamp examination, infrared imaging, confocal microscopy, and optical coherence tomography. This paradigm shift promises enhanced accuracy and consistency in disease evaluation and severity classification. While AI has achieved preliminary strides in meibomian gland evaluation, ongoing advancements in system development and clinical validation are imperative. We review the evolution of MG evaluation, juxtapose AI-driven methods with traditional approaches, elucidate the specific roles of diverse AI technologies, and explore their practical applications using various evaluation techniques. Moreover, we delve into critical considerations for the clinical deployment of AI technologies and envisages future prospects, providing novel insights into MG evaluation and fostering technological and clinical progress in this arena.

Diabetic macular edema (DME) and neovascular age-related macular degeneration (nAMD) are multifactorial disorders that affect the macula and cause significant vision loss. Although inflammation and neoangiogenesis are hallmarks of DME and nAMD, respectively, they share some biochemical mediators. While inflammation is a trigger for the processes that lead to the development of DME, in nAMD inflammation seems to be the consequence of retinal pigment epithelium and Bruch membrane alterations. These pathophysiologic differences may be the key issue that justifies the difference in treatment strategies. Vascular endothelial growth factor inhibitors have changed the treatment of both diseases, however, many patients with DME fail to achieve the established therapeutic goals. From a clinical perspective, targeting inflammatory pathways with intravitreal corticosteroids has been proven to be effective in patients with DME. On the contrary, the clinical relevance of addressing inflammation in patients with nAMD has not been proven yet. We explore the role and implication of inflammation in the development of nAMD and DME and its therapeutical relevance.

We reevaluate acute retinal pigment epitheliitis (ARPE) first described by Krill and Deutman in 1972, integrating a meticulous literature review with advanced multimodal imaging analyses. Our review included 98 eyes from 86 published cases diagnosed with ARPE. We scrutinized ARPE's clinical presentations, variability, and imaging characteristics, revealing that a large majority (90 %) of cases previously diagnosed as ARPE align more closely with other retinal disorders based on modern diagnostic criteria and imaging techniques. Only a small fraction (5 eyes) did not fit into any known categories, casting doubt on ARPE's distinct existence. This underscores the critical role of multimodal imaging in redefining our understanding of macular diseases and challenges the historical classification of ARPE as a unique clinical entity.

Ophthalmic treatment demands precision and consistency in delivering therapeutic agents over extended periods to address many conditions, from common eye disorders to complex diseases. This diversity necessitates a range of delivery strategies, each tailored to specific needs. We delve into various delivery cargos that are pivotal in ophthalmic care. These cargos encompass biodegradable implants that gradually release medication, nonbiodegradable implants for sustained drug delivery, refillable tools allowing flexibility in treatment, hydrogels capable of retaining substances while maintaining ocular comfort, and advanced nanotechnology devices that precisely target eye tissues. Within each cargo category, we explore cutting-edge research-level approaches and FDA-approved methods, providing a thorough overview of the current state of ophthalmic drug delivery. In particular, our focus on nanotechnology reveals the promising potential for gene delivery, cell therapy administration, and the implantation of active devices directly into the retina. These advancements hold the key to more effective, personalized, and minimally- invasive ophthalmic treatments, revolutionizing the field of eye care.

Retinal vein occlusion (RVO) and cerebrovascular disease share common risk factors and may be independently associated; however, the strength and nature of this association remain unclear. We conducted a systematic review and meta-analysis, informed by studies from PubMed, Scopus, EMBASE, Web of Science, and Google Scholar until January 6, 2024, aimed to clarify this relationship. Eligible studies included cohorts observing stroke incidence in RVO patients for over a year. Pooled effect estimates were calculated using random-effects models, with subgroup analyses evaluating associations between RVO types (central and branch) and stroke subtypes (ischemic and hemorrhagic). Ten cohort studies with a total of 428,650 participants (86,299 RVO patients) were included. Compared to controls, RVO patients exhibited a significantly increased risk of stroke (pooled risk ratio [RR]=1.38, 95 % confidence interval (95 %CI)=1.34–1.41). Subgroup analyses indicated elevated risk for both ischemic (RR=1.37, 95 %CI=1.32–1.42) and hemorrhagic (RR=1.55, 95 %CI=1.08–2.22) strokes in RVO patients. Additionally, both central (RR=1.50, 95 %CI=1.27–1.78) and branch (RR=1.41, 95 %CI=1.32–1.50) RVO were associated with stroke risk. Sensitivity analyses confirmed consistent results across various criteria, and funnel plots indicated no publication bias. RVO significantly increases the risk of both ischemic and hemorrhagic stroke, regardless of RVO type, suggesting a strong independent association between these conditions.

The science of diabetes care has progressed to provide a better understanding of the oxidative and inflammatory lesions and pathophysiology of the neurovascular unit within the retina (and brain) that occur early in diabetes, even prediabetes. Screening for retinal structural abnormalities, has traditionally been performed by fundus examination or color fundus photography; however, these imaging techniques detect the disease only when there are sufficient lesions, predominantly hemorrhagic, that are recognized to occur late in the disease process after significant neuronal apoptosis and atrophy, as well as microvascular occlusion with alterations in vision. Thus, interventions have been primarily oriented toward the later-detected stages, and clinical trials, while demonstrating a slowing of the disease progression, demonstrate minimal visual improvement and modest reduction in the continued loss over prolonged periods. Similarly, vision measurement utilizing charts detects only problems of visual function late, as the process begins most often parafoveally with increasing number and progressive expansion, including into the fovea. While visual acuity has long been used to define endpoints of visual function for such trials, current methods reviewed herein are found to be imprecise. We review improved methods of testing visual function and newer imaging techniques with the recommendation that these must be utilized to discover and evaluate the injury earlier in the disease process, even in the prediabetic state. This would allow earlier therapy with ocular as well as systemic pharmacologic treatments that lower the and neuro-inflammatory processes within eye and brain. This also may include newer, micropulsed laser therapy that, if applied during the earlier cascade, should result in improved and often normalized retinal function without the adverse treatment effects of standard photocoagulation therapy.

Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in “detection”, “classification”, and “segmentation” of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96 % in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.

Diabetic macular edema (DME), defined as retinal thickening near, or involving the fovea caused by fluid accumulation in the retina, can lead to vision impairment and blindness in patients with diabetes. Current knowledge of retina anatomy and function and DME pathophysiology has taken great advantage of the availability of several techniques for visualizing the retina. Combining these techniques in a multimodal imaging approach to DME is recommended to improve diagnosis and to guide treatment decisions. We review the recent literature about the following retinal imaging technologies: optical coherence tomography (OCT), OCT angiography (OCTA), wide-field and ultrawide-field techniques applied to fundus photography, fluorescein angiography, and OCTA. The emphasis will be on characteristic DME features identified by these imaging technologies and their potential or established role as diagnostic, prognostic, or predictive biomarkers. The role of artificial intelligence in the assessment and interpretation of retina images is also discussed.

Vitreous, one of the largest components of the human eye, mostly contains water. Despite decades of studying the vitreous structure, numerous unanswered questions still remain, fueling ongoing active research. We attempt to provide a comprehensive overview of the current understanding of the development, morphology, biochemical composition, and function of the vitreous. We emphasize the impact of the vitreous structure and composition on the distribution of drugs. Fast-developing imaging technologies, such as modern optical coherence tomography, unlocked multiple new approaches, offering the potential for in vivo study of the vitreous structure. They allowed to analyze in vivo a range of vitreous structures, such as posterior precortical vitreous pockets, Cloquet canal, channels that interconnect them, perivascular vitreous fissures, and cisterns. We provide an overview of such imaging techniques and their principles and of some challenges in visualizing vitreous structures. Finally, we explores the potential of combining the latest technologies and machine learning to enhance our understanding of vitreous structures.