Hasil untuk "Ophthalmology"

T. Wong, Jennifer K. Sun, R. Kawasaki et al.

Diabetes mellitus (DM) is a global epidemic and affects populations in both developing and developed countries, with differing health care and resource levels. Diabetic retinopathy (DR) is a major complication of DM and a leading cause of vision loss in working middle-aged adults. Vision loss from DR can be prevented with broad-level public health strategies, but these need to be tailored to a country's and population's resource setting. Designing DR screening programs, with appropriate and timely referral to facilities with trained eye care professionals, and using cost-effective treatment for vision-threatening levels of DR can prevent vision loss. The International Council of Ophthalmology Guidelines for Diabetic Eye Care 2017 summarize and offer a comprehensive guide for DR screening, referral and follow-up schedules for DR, and appropriate management of vision-threatening DR, including diabetic macular edema (DME) and proliferative DR, for countries with high- and low- or intermediate-resource settings. The guidelines include updated evidence on screening and referral criteria, the minimum requirements for a screening vision and retinal examination, follow-up care, and management of DR and DME, including laser photocoagulation and appropriate use of intravitreal anti-vascular endothelial growth factor inhibitors and, in specific situations, intravitreal corticosteroids. Recommendations for management of DR in patients during pregnancy and with concomitant cataract also are included. The guidelines offer suggestions for monitoring outcomes and indicators of success at a population level.

D. Ting, L. Peng, A. Varadarajan et al.

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.

Tracy H. T. Lai, E. Tang, S. Chau et al.

Purpose Coronavirus disease (COVID-19) has rapidly emerged as a global health threat. The purpose of this article is to share our local experience of stepping up infection control measures in ophthalmology to minimise COVID-19 infection of both healthcare workers and patients. Methods Infection control measures implemented in our ophthalmology clinic are discussed. The measures are based on detailed risk assessment by both local ophthalmologists and infection control experts. Results A three-level hierarchy of control measures was adopted. First, for administrative control, in order to lower patient attendance, text messages with an enquiry phone number were sent to patients to reschedule appointments or arrange drug refill. In order to minimise cross-infection of COVID-19, a triage system was set up to identify patients with fever, respiratory symptoms, acute conjunctivitis or recent travel to outbreak areas and to encourage these individuals to postpone their appointments for at least 14 days. Micro-aerosol generating procedures, such as non-contact tonometry and operations under general anaesthesia were avoided. Nasal endoscopy was avoided as it may provoke sneezing and cause generation of droplets. All elective clinical services were suspended. Infection control training was provided to all clinical staff. Second, for environmental control, to reduce droplet transmission of COVID-19, installation of protective shields on slit lamps, frequent disinfection of equipment, and provision of eye protection to staff were implemented. All staff were advised to measure their own body temperatures before work and promptly report any symptoms of upper respiratory tract infection, vomiting or diarrhoea. Third, universal masking, hand hygiene, and appropriate use of personal protective equipment (PPE) were promoted. Conclusion We hope our initial experience in stepping up infection control measures for COVID-19 infection in ophthalmology can help ophthalmologists globally to prepare for the potential community outbreak or pandemic. In order to minimise transmission of COVID-19, ophthalmologists should work closely with local infection control teams to implement infection control measures that are appropriate for their own clinical settings.

S. Resnikoff, V. Lansingh, L. Washburn et al.

Background/aims To estimate 2015 global ophthalmologist data and analyse their relationship to income groups, prevalence rates of blindness and visual impairment and gross domestic product (GDP) per capita. Methods Online surveys were emailed to presidents/chairpersons of national societies of ophthalmology and Ministry of Health representatives from all 194 countries to capture the number and density (per million population) of ophthalmologists, the number/density performing cataract surgery and refraction, and annual ophthalmologist population growth trends. Correlations between these data and income group, GDP per capita and prevalence rates of blindness and visual impairment were analysed. Results In 2015, there were an estimated 232 866 ophthalmologists in 194 countries. Income was positively associated with ophthalmologist density (a mean 3.7 per million population in low-income countries vs a mean 76.2 in high-income countries). Most countries reported positive growth (94/156; 60.3%). There was a weak, inverse correlation between the prevalence of blindness and the ophthalmologist density. There were weak, positive correlations between the density of ophthalmologists performing cataract surgery and GDP per capita and the prevalence of blindness, as well as between GDP per capita and the density of ophthalmologists doing refractions. Conclusions Although the estimated global ophthalmologist workforce appears to be growing, the appropriate distribution of the eye care workforce and the development of comprehensive eye care delivery systems are needed to ensure that eye care needs are universally met.

Sophia M Saleem, L. Pasquale, P. Sidoti et al.

Purpose To discuss the effects of the severe acute respiratory syndrome coronavirus 2 betacoronavirus on ambulatory ophthalmology practices, the value proposition of telemedicine, teleophthalmology implementation methodologies, and the accelerated future of telemedicine. Design Review of the current telehealth landscape including usage, policies, and techniques for ambulatory practice integration. Methods We provide author-initiated review of recent trends in telehealth, governmental recommendations for health care delivery during the COVID-19 pandemic, and a PubMed Central query for telemedicine in ophthalmology or teleophthalmology. In addition, the authors' comprehensive experience in telemedicine design and implementation is provided. Results We provide a summary describing the present state of telehealth, teleophthalmology modeling, care delivery, and the proposed impact of telehealth surges on the future of ophthalmology practice. Conclusion Recent patient and provider interest in telemedicine, the relaxation of regulatory restrictions, increased remote care reimbursement, and ongoing social distancing practices compel many ophthalmologists to consider virtualizing services.

Aram You, Jin Kuk Kim, I. Ryu et al.

Background Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. Methods We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. Results In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. Conclusions The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.

J. Scheetz, Philip Rothschild, M. McGuinness et al.

Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June–August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.

Ehsan Misaghi, Sean T Berkowitz, Bing Yu Chen et al.

Large language models (LLMs) show potential for ophthalmic clinical reasoning, yet individual models risk introducing harm. We evaluated whether multi-agent LLM deliberative councils improve diagnostic performance and mitigate harm compared to individual LLMs. In a comparative cross-sectional study, we assessed 12 individual LLMs and three multi-agent councils on 100 ophthalmology clinical vignettes. Each council comprised four models assembled by type: proprietary flagship, proprietary fast, and open-source. Models independently answered a vignette, anonymously ranked one another's responses, and a designated chair synthesized all responses and peer reviews into a final answer. Councils consistently outperformed pooled individual models across all three tiers. Accuracy improved for proprietary flagship (95.0% vs 90.8%; risk difference [RD]: 4.25 [95% CI: 0.45, 8.05]), proprietary fast (96.0% vs 86.5%; RD: 9.50 [5.31, 13.59]), and open-source councils (91.0% vs 83.2%; RD: 7.75 [4.17, 11.33]). Harm rates declined for proprietary flagship (10.0% vs 22.5%; RD: -12.50 [-16.86, -8.14]), proprietary fast (16.0% vs 31.8%; RD: -15.75 [-21.49, -10.01]), and open-source councils (22.0% vs 38.5%; RD: -16.50 [-22.27, -10.73]). Coverage analysis revealed net positive gains for accuracy (ΔCoverage: 4.4-9.8 percentage points) and safety (ΔCoverage: 13.6-20.6), indicating councils recovered correct diagnoses and averted harm. Councils elevated correct diagnoses to higher rank positions; and produced more complete differentials and management plans (all P<.05). Harmful council responses showed reduced combined commission-and-omission errors and tended to be less severe. Structured deliberation via multi-agent LLM councils may enhance the reliability of LLM-assisted ophthalmic clinical reasoning.

J. Németh, B. Tapasztó, W. Aclimandos et al.

The prevalence of myopia is increasing extensively worldwide. The number of people with myopia in 2020 is predicted to be 2.6 billion globally, which is expected to rise up to 4.9 billion by 2050, unless preventive actions and interventions are taken. The number of individuals with high myopia is also increasing substantially and pathological myopia is predicted to become the most common cause of irreversible vision impairment and blindness worldwide and also in Europe. These prevalence estimates indicate the importance of reducing the burden of myopia by means of myopia control interventions to prevent myopia onset and to slow down myopia progression. Due to the urgency of the situation, the European Society of Ophthalmology decided to publish this update of the current information and guidance on management of myopia. The pathogenesis and genetics of myopia are also summarized and epidemiology, risk factors, preventive and treatment options are discussed in details.

T. F. Tan, Yong Li, J. Lim et al.

Abstract The outbreak of the coronavirus disease 2019 has further increased the urgent need for digital transformation within the health care settings, with the use of artificial intelligence/deep learning, internet of things, telecommunication network/virtual platform, and blockchain. The recent advent of metaverse, an interconnected online universe, with the synergistic combination of augmented, virtual, and mixed reality described several years ago, presents a new era of immersive and real-time experiences to enhance human-to-human social interaction and connection. In health care and ophthalmology, the creation of virtual environment with three-dimensional (3D) space and avatar, could be particularly useful in patient-fronting platforms (eg, telemedicine platforms), operational uses (eg, meeting organization), digital education (eg, simulated medical and surgical education), diagnostics, and therapeutics. On the other hand, the implementation and adoption of these emerging virtual health care technologies will require multipronged approaches to ensure interoperability with real-world virtual clinical settings, user-friendliness of the technologies and clinical efficiencies while complying to the clinical, health economics, regulatory, and cybersecurity standards. To serve the urgent need, it is important for the eye community to continue to innovate, invent, adapt, and harness the unique abilities of virtual health care technology to provide better eye care worldwide.

Siddharth Nath, Abdullah Marie, S. Ellershaw et al.

Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.

Ting Fang Tan, A. Thirunavukarasu, J. Campbell et al.

The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosure(s) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Zhenyue Qin, Yang Liu, Yu Yin et al.

Vision-threatening eye diseases pose a major global health burden, with timely diagnosis limited by workforce shortages and restricted access to specialized care. While multimodal large language models (MLLMs) show promise for medical image interpretation, advancing MLLMs for ophthalmology is hindered by the lack of comprehensive benchmark datasets suitable for evaluating generative models. We present a large-scale multimodal ophthalmology benchmark comprising 32,633 instances with multi-granular annotations across 12 common ophthalmic conditions and 5 imaging modalities. The dataset integrates imaging, anatomical structures, demographics, and free-text annotations, supporting anatomical structure recognition, disease screening, disease staging, and demographic prediction for bias evaluation. This work extends our preliminary LMOD benchmark with three major enhancements: (1) nearly 50% dataset expansion with substantial enlargement of color fundus photography; (2) broadened task coverage including binary disease diagnosis, multi-class diagnosis, severity classification with international grading standards, and demographic prediction; and (3) systematic evaluation of 24 state-of-the-art MLLMs. Our evaluations reveal both promise and limitations. Top-performing models achieved ~58% accuracy in disease screening under zero-shot settings, and performance remained suboptimal for challenging tasks like disease staging. We will publicly release the dataset, curation pipeline, and leaderboard to potentially advance ophthalmic AI applications and reduce the global burden of vision-threatening diseases.

Duy M. H. Nguyen, Hasan Md Tusfiqur Alam, Tai Nguyen et al.

The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagnosis and treatment of posterior segment eye diseases. This review explores the cutting-edge applications of DL across a range of ocular conditions, including diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation. We provide a comprehensive overview of foundational ML techniques and advanced DL architectures, such as CNNs, attention mechanisms, and transformer-based models, highlighting the evolving role of AI in enhancing diagnostic accuracy, optimizing treatment strategies, and improving overall patient care. Additionally, we present key challenges in integrating AI solutions into clinical practice, including ensuring data diversity, improving algorithm transparency, and effectively leveraging multimodal data. This review emphasizes AI's potential to improve disease diagnosis and enhance patient care while stressing the importance of collaborative efforts to overcome these barriers and fully harness AI's impact in advancing eye care.

Qingshan Hou, Yukun Zhou, Jocelyn Hui Lin Goh et al.

The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by fine-tuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850-0.952 vs 0.823-0.944, across three datasets, all P<=0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P<0.001). In glaucoma, DINOv2-base model outperformed RETFound (AUROC=0.958 vs 0.940, P<0.001). Conversely, RETFound achieved superior performance over all DINOv2 models in predicting heart failure, myocardial infarction, and ischaemic stroke (AUROC=0.732-0.796 vs 0.663-0.771, all P<0.001). These trends persisted even with 10% of the fine-tuning data. These findings showcase the distinct scenarios where general-purpose and domain-specific FMs excel, highlighting the importance of aligning FM selection with task-specific requirements to optimise clinical performance.

Sijing Li, Tianwei Lin, Lingshuai Lin et al.

Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare, but their reliance on general medical data and coarse-grained global visual understanding limits them in intelligent ophthalmic diagnosis. Currently, intelligent ophthalmic diagnosis faces three major challenges: (i) Data. The lack of deeply annotated, high-quality, multi-modal ophthalmic visual instruction data; (ii) Benchmark. The absence of a comprehensive and systematic benchmark for evaluating diagnostic performance; (iii) Model. The difficulty of adapting holistic visual architectures to fine-grained, region-specific ophthalmic lesion identification. In this paper, we propose the Eyecare Kit, which systematically tackles the aforementioned three key challenges with the tailored dataset, benchmark and model: First, we construct a multi-agent data engine with real-life ophthalmology data to produce Eyecare-100K, a high-quality ophthalmic visual instruction dataset. Subsequently, we design Eyecare-Bench, a benchmark that comprehensively evaluates the overall performance of LVLMs on intelligent ophthalmic diagnosis tasks across multiple dimensions. Finally, we develop the EyecareGPT, optimized for fine-grained ophthalmic visual understanding thoroughly, which incorporates an adaptive resolution mechanism and a layer-wise dense connector. Extensive experimental results indicate that the EyecareGPT achieves state-of-the-art performance in a range of ophthalmic tasks, underscoring its significant potential for the advancement of open research in intelligent ophthalmic diagnosis. Our project is available at https://github.com/DCDmllm/EyecareGPT.

Minjie Zou, Sahana Srinivasan, Thaddaeus Wai Soon Lo et al.

Recent advances in reasoning-focused large language models (LLMs) mark a shift from general LLMs toward models designed for complex decision-making, a crucial aspect in medicine. However, their performance in specialized domains like ophthalmology remains underexplored. This study comprehensively evaluated and compared the accuracy and reasoning capabilities of four newly developed reasoning-focused LLMs, namely DeepSeek-R1, OpenAI o1, o3-mini, and Gemini 2.0 Flash-Thinking. Each model was assessed using 5,888 multiple-choice ophthalmology exam questions from the MedMCQA dataset in zero-shot setting. Quantitative evaluation included accuracy, Macro-F1, and five text-generation metrics (ROUGE-L, METEOR, BERTScore, BARTScore, and AlignScore), computed against ground-truth reasonings. Average inference time was recorded for a subset of 100 randomly selected questions. Additionally, two board-certified ophthalmologists qualitatively assessed clarity, completeness, and reasoning structure of responses to differential diagnosis questions.O1 (0.902) and DeepSeek-R1 (0.888) achieved the highest accuracy, with o1 also leading in Macro-F1 (0.900). The performance of models across the text-generation metrics varied: O3-mini excelled in ROUGE-L (0.151), o1 in METEOR (0.232), DeepSeek-R1 and o3-mini tied for BERTScore (0.673), DeepSeek-R1 (-4.105) and Gemini 2.0 Flash-Thinking (-4.127) performed best in BARTScore, while o3-mini (0.181) and o1 (0.176) led AlignScore. Inference time across the models varied, with DeepSeek-R1 being slowest (40.4 seconds) and Gemini 2.0 Flash-Thinking fastest (6.7 seconds). Qualitative evaluation revealed that DeepSeek-R1 and Gemini 2.0 Flash-Thinking tended to provide detailed and comprehensive intermediate reasoning, whereas o1 and o3-mini displayed concise and summarized justifications.

Suvijak Untaaveesup, MD, Thipsukon Amnartpanich, MD, Noraworn Jirattikanwong, MD et al.

Background: Chronic systemic inflammation in individuals with moderate-to-severe atopic dermatitis (AD) potentially predisposes them to metabolic and cardiovascular diseases. Nevertheless, evidence with regard to such association is limited. Objective: To assess the association between metabolic and cardiovascular outcomes and moderate-to-severe AD. Methods: A systematic search was performed through PubMed, Scopus, EMBASE, and Cochrane for population-based studies that addressed the effects of moderate-to-severe AD on metabolic and cardiovascular outcomes compared with the general population from inception to August 31, 2023. Meta-analysis was performed using the random effects model. The pooled odds ratio (OR) and certainty of evidence for each outcome were reported. Results: We included 11 studies, 4 retrospective cohorts, 1 prospective cohort, 4 cross-sectional, and 2 case-control studies involving 405,170 moderate-to-severe AD patients compared to 4,591,478 unaffected controls. Moderate-to-severe AD was associated with a higher risk of myocardial infarction with an OR (95% CI) of 1.33 (1.07, 1.65), angina 1.33 (1.06, 1.66), heart failure 1.56 (1.28, 1.90), stroke 1.45 (1.21, 1.74), hypertension 1.38 (1.18, 1.63), dyslipidemia 1.27 (1.15, 1.41), and metabolic syndrome 1.24 (1.05, 1.42) with very low certainty of evidence. No significantly increased risk of cardiovascular death with an odds ratio (95% CI) of 1.81 (0.96, 3.44) and diabetes of 1.24 (0.91, 1.68) was observed. High heterogeneity was observed in most studies for all of the outcomes. Conclusion: Our meta-analysis demonstrated a modest but significant association between moderate-to-severe AD and increased susceptibility to metabolic and cardiovascular diseases. Initial assessment of cardiovascular and metabolic risk for patients with moderate-to-severe AD should be considered to enable early management strategies.

Ashot A. Avagimyan, Mohammad Sheibani, Artem I. Trofimenko et al.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are now uncovering new possibilities in the field of internal medicine owing to their diverse protective effects. In the second part of the literature review, we explore potential applications of SGLT2i in hepatology, neurology, ophthalmology, and oncology, mechanisms of action of such drugs as dapagliflozin, empagliflozin, canagliflozin, etc, and their effect on different organs and systems.

Emanuela Longo, Elena Giuliano, Agnese Gagliardi et al.

In ophthalmology, developing effective drug delivery systems is crucial to overcome anatomical and physiological barriers, such as rapid tear turnover and blinking, which limit the efficacy of conventional formulations like eye drops. Poloxamers, especially the derivatives 407 (P407) and 188, are amphiphilic triblock copolymers characterized by an intriguing thermo-reversible behavior, making them ideal candidates for the development of in situ hydrogels for ocular applications. Various thermo-sensitive poloxamer-based hydrogels were designed to be easily instilled as liquids at room temperature, gelling promptly upon contact with the corneal surface. These systems promoted a controlled release of active compounds, significantly improving their adhesion to the ocular surface. This review discusses the most relevant scientific literature on the topic, with particular attention to studies published in recent years. The results demonstrated that poloxamer formulations are capable of overcoming typical ocular barriers, thereby increasing drug bioavailability. The intrinsic biocompatibility of poloxamers contributes to the safety and tolerability of the system. Furthermore, P407 showed additional wound healing features. The combination of biocompatibility and thermo-reversible behavior makes poloxamer-based hydrogels a promising platform for the development of innovative ocular drug delivery systems able to enhance therapeutic efficacy and patient comfort.