Current Opinion in Ophthalmology最新文献

Purpose of review: This review aims to cover the current landscape of antifibrotics used in glaucoma surgery and discuss developing antifibrotic agents. This review will inform the reader of new antifibrotic agents in development, clinical trials and clinical use that may alter the standard of care in glaucoma surgery in the near future.

Recent findings: Mitomycin-C (MMC) remains the most commonly used antifibrotic in glaucoma surgery to date with expanding use beyond trabeculectomy into the world of minimally invasive bleb forming surgeries. MMC continues to cause similar side effects due to toxicity which is a main driver of innovation. Newer antifibrotic agents are under investigation at all stages of drug development from bench research to clinical use. Familiar agents such as bevacizumab, sodium hyaluronate, and matrix metalloproteinases have shown noninferior success rates to MMC when used as adjunct agents with filtration surgery. Many other antifibrotics agents are being investigated with mixed results.

Summary: While MMC remains the gold standard antifibrotic agent for glaucoma surgery, there are numerous antifibrotic agents in development with safer side effect profiles and similar success rates that may change the surgical practice of glaucoma.

Purpose of review: Several high-quality prospective clinical trials have emerged in recent years that have provided evidence to support changes in management of glaucoma. This article reviews results from five recent landmark studies and discusses how they may impact clinical practice.

Recent findings: Five multicenter trials - LiGHT, ZAP, EAGLE, TAGS, and PTVT - have reshaped glaucoma management strategies. LiGHT demonstrated that selective laser therapy is a well tolerated and effective first-line therapy for open-angle glaucoma. ZAP investigated routine use of prophylactic laser peripheral iridotomy, emphasizing the importance of risk stratification in primary angle closure suspects. EAGLE established clear-lens exchange as a viable alternative to conventional treatment in select patients with angle closure anatomy. TAGS showed that primary trabeculectomy provides better intraocular pressure (IOP) control and reduced visual field progression in advanced disease. Finally, PTVT highlighted that both trabeculectomy and tube shunt surgeries remain appropriate initial surgeries, with procedure choice best guided by baseline IOP, risk tolerance, and patient goals.

Summary: Improvements in laser and surgical technology, increased understanding of the natural history of glaucoma, and evidence regarding the importance of early intervention in disease have led to fundamental changes in glaucoma practice. The five clinical trials reviewed in this article support these shifts and provide frameworks to guide clinical practice.

Purpose of review: Intravitreal antivascular endothelial growth factor (VEGF) injections are the cornerstone of retinal disease management but raise concern for intraocular pressure (IOP)-related complications. This review summarizes recent evidence on transient and sustained IOP elevation, structural and vascular effects, and management strategies.

Recent findings: Acute IOP spikes occur in nearly all eyes, typically resolving within an hour, but recovery is delayed in glaucoma and ocular hypertension (OHT), increasing optic nerve risk. Spike magnitude depends on patient factors (age, diabetes, vitreous volume, lens status) and technical factors (needle gauge, reflux, injection volume). Sustained IOP elevation is less predictable, with higher rates for bevacizumab and ranibizumab than aflibercept, and cumulative injection burden correlating with reduced outflow facility, retinal nerve fiber layer (RNFL) thinning, and greater need for glaucoma surgery. Acute spikes cause immediate RNFL thinning, while long-term loss is uncommon in nonglaucomatous patients. Prophylactic IOP-lowering therapy, paracentesis in high-risk eyes, and agent or delivery selection may mitigate risk.

Summary: Uniform protocols inadequately address patient-specific factors, warranting individualized management strategies. Prophylactic measures, careful agent selection, and multidisciplinary management can mitigate IOP-related complications in susceptible patients.

Purpose of review: This review aims to highlight the expanding role of big data in ophthalmology, provide a comparison of the most prominent databases, and their use in glaucoma-specific research. Understanding the strengths and limitations of each database allows researchers to tailor their research questions appropriately.

Recent findings: Several large-scale databases have emerged in ophthalmology research. Some databases offer detailed ocular exam findings and imaging, supporting artificial intelligence-driven diagnostics and treatment evaluation. Others are broader in scope, providing real-world population data to study trends and outcomes. Some databases even integrate genomic and systemic data, enabling novel explorations of disease risk and health disparities. Collectively, big data has enabled large-scale studies on a wide range of topics, advancing the field of ophthalmology in every aspect.

Summary: Big data platforms are transforming ophthalmology research, from uncovering systemic-ocular relationships to enabling artificial intelligence applications. Researchers can select platforms based on the availability of imaging, systemic data, or genomic information to better address specific research questions. Doing so can enhance precision medicine, address care disparities, and drive innovation in disease pathophysiology discovery, detection, and management.

Purpose of review: Conventional glaucoma drainage devices (GDDs), such as the Ahmed Glaucoma Valve and Baerveldt Glaucoma Implant, are widely used in childhood glaucoma. Recently, newer surgical options, including the PRESERFLO microshunt, Paul Glaucoma Implant (PGI), eyePlate-S, and ClearPath, have emerged with potential advantages to childhood glaucoma patients. This review summarizes current evidence regarding the design, outcomes, and safety profiles of these newer implants in childhood glaucoma patients.

Recent findings: The PRESERFLO offers a minimally invasive approach with favorable safety and intraocular pressure (IOP)-lowering outcomes in small pediatric case series, including eyes with prior GDD implantation. It is typically used with adjunctive mitomycin C. Technical modifications, such as ripcord insertion, reduce the risk of postoperative hypotony.The PGI features a valveless design that enables controlled flow via a 6-0 Prolene ripcord without external ligation. Additionally, its design reduces contact with the corneal endothelium and extraocular muscles. Retrospective series and early randomized data suggest good IOP control with an acceptable safety profile. The eyePlate-S features a valveless design that allows controlled flow through a 5-0 Prolene ripcord, eliminating the need for external ligation. Its tube has an external diameter comparable to the PGI (0.47 mm) but a larger internal diameter (0.18 mm in the eyePlate-S vs. 0.13 mm in the PGI). The eyePlate-200S can also be positioned between extraocular muscles, which may reduce the risk of postoperative diplopia. In addition, the thin, flexible silicone plate can be folded to facilitate implantation.The ClearPath enables flexible implantation in complex pediatric anatomy. Early multiyear results show sustained IOP reduction and decreased medication burden, with a success rate of 79% at 4 years.

Summary: Newer devices such as the PRESERFLO, PGI, and ACP demonstrate encouraging mid-term efficacy and safety in the management of childhood glaucoma, particularly in refractory cases or eyes with prior surgeries. While early outcomes are promising, larger comparative studies with extended follow-up are needed to better establish their long-term role relative to conventional GDDs in childhood glaucoma management.

Purpose of review: This review examines the emerging role of artificial intelligence (AI) in the detection and management of pediatric retinal diseases, with a focus on systems that achieve expert-level performance in classifying fundus abnormalities. We highlight models developed for clinical application as assistive or autonomous tools with the potential to enhance early identification and referral, streamline care delivery, and improve access to care.

Recent findings: AI systems, have shown high diagnostic accuracy in identifying retinal pathology associated with retinopathy of prematurity, pediatric myopia, diabetic retinopathy, and retinoblastoma. Many of these systems have potential utility in real-world screening, supplementing clinical decision-making, and guiding early intervention. In addition, autonomous AI systems can increase access in low-resource, remote settings and areas where access to pediatric ophthalmologists is limited. Ongoing advances include integration with nonmydriatic fundus photography, smartphone-based imaging, and image-free diagnostic modalities, further expanding reach and applicability.

Summary: AI holds transformative promise for pediatric retina care by providing scalable, accurate, and accessible screening solutions. These systems have demonstrated to enhance clinical expertise, minimize interobserver variability, facilitate timely referrals and support decision-making. As integration of algorithms into clinical and community settings is established, AI is poised to become an essential component of pediatric ophthalmology, improving early detection and reducing the global burden of preventable childhood blindness.

Purpose of review: With the rise of 'oculomics' and the application of advanced artificial intelligence techniques in healthy ageing, retinal imaging, the only way we can directly visualize the microvascular circulation, is expanding beyond ophthalmology into broader systemic health monitoring. The purpose of this review is to summarize recent advances in this rapidly evolving field and assess the opportunities, challenges, and future directions of the use of oculomics in translating into real-world clinical use.

Recent findings: Retinal imaging modalities, such as color fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), and wide-field imaging, are increasingly integrated with deep learning algorithms to detect, predict, and manage a broad spectrum of systemic diseases, including cardiovascular, cerebrovascular, renal, metabolic, and neurodegenerative disorders, as well as less commonly studied conditions. While research in more established areas is beginning to address clinical translation and implementation, significant challenges remain before these technologies can be reliably adopted in long-term, real-world healthcare settings.

Summary: Artificial intelligence applied to retinal imaging has matured from proof-of-concept classifiers to externally validated, occasionally regulated tools that noninvasively profile systemic conditions. Multiplexed foundation models and multimodal transformers herald a shift toward holistic 'oculomics' platforms, yet prospective multicenter trials, equitable performance auditing, and health-economic evaluations are essential before widescale clinical adoption.

Purpose of review: The current article provides an overview of the utility of artificial intelligence approaches to aid in the design, recruitment, execution, and dissemination of ophthalmic clinical trials.

Recent findings: Within the last decade, artificial intelligence has heralded a new age for ophthalmology, with novel applications habitually appearing within the literature. Though clinical trials are considered the gold standard for driving evidence-based practice, remarkably few studies have examined the potential for machine learning to augment the clinical trial pipeline. Clinical trials within ophthalmology often do not reach planned endpoints due to insufficient enrolment, cost overruns, and can lack reliability from unblinded outcome assessors. Ones that do, frequently take longer to enroll patients than intended. Artificial intelligence-based approaches have recently been shown to be effective in identifying eligible clinical trial participants using both imaging and text data.

Summary: Given the key role of clinical trials in the advancement of ophthalmic clinical practice, trialists should consider the potential for artificial intelligence-powered tools to enhance the design, recruitment, and delivery of future studies.

Purpose of review: To highlight the ocular manifestations associated with COVID-19 and review the evidence surrounding proposed associations with corneal transplant rejection and uveitis reactivation.

Recent findings: SARS-CoV-2 has been associated with various ocular manifestations, most commonly conjunctivitis. Less frequently, more severe complications such as uveitis, retinal vascular occlusions, and neuro-ophthalmic syndromes have been reported. These manifestations are theorized to occur secondary to both direct viral invasion as well as secondary systemic inflammation and immune dysregulation. While case reports suggest a possible link between COVID-19 infection and vaccination and corneal graft rejection, large cohort studies and meta-analyses have not confirmed an association. In contrast, retrospective data suggest a slight increase in uveitis recurrence following COVID-19 infection and vaccination in patients with prior uveitis.

Summary: Ocular involvement in COVID-19 ranges from mild conjunctivitis to rare, vision-threatening inflammatory and vascular complications. While case reports have suggested temporal links between COVID-19 infection or vaccination and corneal transplant rejection or uveitis reactivation, current evidence does not support an association with graft rejection, and only a slight increase in uveitis recurrence has been observed in certain high-risk populations. With longer follow-up and prospective analyses, a clearer understanding of underlying mechanisms and risk factors may emerge to better guide ophthalmic management during and beyond the pandemic.

Purpose of review: Alongside the development of large language models (LLMs) and generative artificial intelligence (AI) applications across a diverse range of clinical applications in Ophthalmology, this review highlights the importance of evaluation of LLM applications by discussing evaluation metrics commonly adopted.

Recent findings: Generative AI applications have demonstrated encouraging performance in clinical applications of Ophthalmology. Beyond accuracy, evaluation in the form of quantitative and qualitative metrics facilitate a more nuanced assessment of LLM output responses. Several challenges limit evaluation including the lack of consensus on standardized benchmarks, and limited availability of robust and curated clinical datasets.

Summary: This review outlines the spectrum of quantitative and qualitative evaluation metrics adopted in existing studies, highlights key challenges in LLM evaluation, to catalyze further work towards standardized and domain-specific evaluation. Robust evaluation to effectively validate clinical LLM applications is crucial in closing the gap towards clinical integration.