Hasil untuk "Ophthalmology"

Ting Fang Tan, Kabilan Elangovan, Andreas Pollreisz et al.

Domain specific large language models are increasingly used to support patient education, triage, and clinical decision making in ophthalmology, making rigorous evaluation essential to ensure safety and accuracy. This study evaluated four small medical LLMs Meerkat-7B, BioMistral-7B, OpenBioLLM-8B, and MedLLaMA3-v20 in answering ophthalmology related patient queries and assessed the feasibility of LLM based evaluation against clinician grading. In this cross sectional study, 180 ophthalmology patient queries were answered by each model, generating 2160 responses. Models were selected for parameter sizes under 10 billion to enable resource efficient deployment. Responses were evaluated by three ophthalmologists of differing seniority and by GPT-4-Turbo using the S.C.O.R.E. framework assessing safety, consensus and context, objectivity, reproducibility, and explainability, with ratings assigned on a five point Likert scale. Agreement between LLM and clinician grading was assessed using Spearman rank correlation, Kendall tau statistics, and kernel density estimate analyses. Meerkat-7B achieved the highest performance with mean scores of 3.44 from Senior Consultants, 4.08 from Consultants, and 4.18 from Residents. MedLLaMA3-v20 performed poorest, with 25.5 percent of responses containing hallucinations or clinically misleading content, including fabricated terminology. GPT-4-Turbo grading showed strong alignment with clinician assessments overall, with Spearman rho of 0.80 and Kendall tau of 0.67, though Senior Consultants graded more conservatively. Overall, medical LLMs demonstrated potential for safe ophthalmic question answering, but gaps remained in clinical depth and consensus, supporting the feasibility of LLM based evaluation for large scale benchmarking and the need for hybrid automated and clinician review frameworks to guide safe clinical deployment.

Jiaqi Yao, Jiaqi Yao, Xinjian Lu et al.

BackgroundCompared to single-shell diffusion tensor imaging (DTI), free water (FW) and neurite orientation dispersion and density imaging (NODDI) offer a more comprehensive evaluation of microstructural alterations in cerebral white matter (WM), particularly in detecting crossing fibers. However, research utilizing multi-shell diffusion imaging to investigate thyroid-associated ophthalmopathy (TAO) remains limited. This study employs FW and NODDI to investigate microstructural changes in the white matter of the visual pathways in patients with TAO.MethodsMulti-shell diffusion magnetic resonance imaging (dMRI) scans were performed on 45 patients with TAO and 31 age- and sex-matched healthy controls (HC). Tract-based spatial statistics (TBSS) analysis was conducted using eight FW and NODDI-derived metrics to identify group differences in white matter microstructure. Furthermore, correlations between these microstructural changes and clinical measures were examined.ResultsTBSS analysis revealed that, compared to HC, patients with TAO exhibited lower free-water corrected fractional anisotropy (fwFA) and free-water corrected axial diffusivity (fwAD), while free-water corrected mean diffusivity (fwMD), free-water corrected radial diffusivity (fwRD), and orientation dispersion index (ODI) were significantly increased (p < 0.05, FWE). Notably, ODI demonstrated the highest area under the curve (AUC) among these metrics. Furthermore, fwFA, fwAD, fwMD, fwRD, and ODI showed significant correlations with the Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), and the Graves’ Orbitopathy Quality of Life Questionnaire (GO-QOL2) scores.ConclusionThis study suggests that abnormalities in the white matter microstructure of TAO patients can be detected through the complementary use of FW and NODDI metrics, and it is revealed that these changes may have an impact on mental health.

Yan Hu, Mingdao Gong, Zhongxi Qiu et al.

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions.

Danli Shi, Bowen Liu, Zhen Tian et al.

Myopia, projected to affect 50% population globally by 2050, is a leading cause of vision loss. Eyes with pathological myopia exhibit distinctive shape distributions, which are closely linked to the progression of vision-threatening complications. Recent understanding of eye-shape-based biomarkers requires magnetic resonance imaging (MRI), however, it is costly and unrealistic in routine ophthalmology clinics. We present Fundus2Globe, the first AI framework that synthesizes patient-specific 3D eye globes from ubiquitous 2D color fundus photographs (CFPs) and routine metadata (axial length, spherical equivalent), bypassing MRI dependency. By integrating a 3D morphable eye model (encoding biomechanical shape priors) with a latent diffusion model, our approach achieves submillimeter accuracy in reconstructing posterior ocular anatomy efficiently. Fundus2Globe uniquely quantifies how vision-threatening lesions (e.g., staphylomas) in CFPs correlate with MRI-validated 3D shape abnormalities, enabling clinicians to simulate posterior segment changes in response to refractive shifts. External validation demonstrates its robust generation performance, ensuring fairness across underrepresented groups. By transforming 2D fundus imaging into 3D digital replicas of ocular structures, Fundus2Globe is a gateway for precision ophthalmology, laying the foundation for AI-driven, personalized myopia management.

Negin Ghamsarian, Raphael Sznitman, Klaus Schoeffmann et al.

To meet the growing demand for systematic surgical training, wet-lab environments have become indispensable platforms for hands-on practice in ophthalmology. Yet, traditional wet-lab training depends heavily on manual performance evaluations, which are labor-intensive, time-consuming, and often subject to variability. Recent advances in computer vision offer promising avenues for automated skill assessment, enhancing both the efficiency and objectivity of surgical education. Despite notable progress in ophthalmic surgical datasets, existing resources predominantly focus on real surgeries or isolated tasks, falling short of supporting comprehensive skill evaluation in controlled wet-lab settings. To address these limitations, we introduce WetCat, the first dataset of wet-lab cataract surgery videos specifically curated for automated skill assessment. WetCat comprises high-resolution recordings of surgeries performed by trainees on artificial eyes, featuring comprehensive phase annotations and semantic segmentations of key anatomical structures. These annotations are meticulously designed to facilitate skill assessment during the critical capsulorhexis and phacoemulsification phases, adhering to standardized surgical skill assessment frameworks. By focusing on these essential phases, WetCat enables the development of interpretable, AI-driven evaluation tools aligned with established clinical metrics. This dataset lays a strong foundation for advancing objective, scalable surgical education and sets a new benchmark for automated workflow analysis and skill assessment in ophthalmology training. The dataset and annotations are publicly available in Synapse.

Xiaoling Luo, Ruli Zheng, Qiaojian Zheng et al.

Visual impairment represents a major global health challenge, with multimodal imaging providing complementary information that is essential for accurate ophthalmic diagnosis. This comprehensive survey systematically reviews the latest advances in multimodal deep learning methods in ophthalmology up to the year 2025. The review focuses on two main categories: task-specific multimodal approaches and large-scale multimodal foundation models. Task-specific approaches are designed for particular clinical applications such as lesion detection, disease diagnosis, and image synthesis. These methods utilize a variety of imaging modalities including color fundus photography, optical coherence tomography, and angiography. On the other hand, foundation models combine sophisticated vision-language architectures and large language models pretrained on diverse ophthalmic datasets. These models enable robust cross-modal understanding, automated clinical report generation, and decision support. The survey critically examines important datasets, evaluation metrics, and methodological innovations including self-supervised learning, attention-based fusion, and contrastive alignment. It also discusses ongoing challenges such as variability in data, limited annotations, lack of interpretability, and issues with generalizability across different patient populations. Finally, the survey outlines promising future directions that emphasize the use of ultra-widefield imaging and reinforcement learning-based reasoning frameworks to create intelligent, interpretable, and clinically applicable AI systems for ophthalmology.

Xiaoyi Liang, Mouxiao Bian, Moxin Chen et al.

In recent years, large language models (LLMs) have demonstrated remarkable potential across various medical applications. Building on this foundation, multimodal large language models (MLLMs) integrate LLMs with visual models to process diverse inputs, including clinical data and medical images. In ophthalmology, LLMs have been explored for analyzing optical coherence tomography (OCT) reports, assisting in disease classification, and even predicting treatment outcomes. However, existing MLLM benchmarks often fail to capture the complexities of real-world clinical practice, particularly in the analysis of OCT images. Many suffer from limitations such as small sample sizes, a lack of diverse OCT datasets, and insufficient expert validation. These shortcomings hinder the accurate assessment of MLLMs' ability to interpret OCT scans and their broader applicability in ophthalmology. Our dataset, curated through rigorous quality control and expert annotation, consists of 439 fundus images and 75 OCT images. Using a standardized API-based framework, we assessed seven mainstream MLLMs and observed significant variability in diagnostic accuracy across different diseases. While some models performed well in diagnosing conditions such as diabetic retinopathy and age-related macular degeneration, they struggled with others, including choroidal neovascularization and myopia, highlighting inconsistencies in performance and the need for further refinement. Our findings emphasize the importance of developing clinically relevant benchmarks to provide a more accurate assessment of MLLMs' capabilities. By refining these models and expanding their scope, we can enhance their potential to transform ophthalmic diagnosis and treatment.

Alejandro Lozano, Min Woo Sun, James Burgess et al.

The development of vision-language models (VLMs) is driven by large-scale and diverse multimodal datasets. However, progress toward generalist biomedical VLMs is limited by the lack of annotated, publicly accessible datasets across biology and medicine. Existing efforts are restricted to narrow domains, missing the full diversity of biomedical knowledge encoded in scientific literature. To address this gap, we introduce BIOMEDICA, a scalable, open-source framework to extract, annotate, and serialize the entirety of the PubMed Central Open Access subset into an easy-to-use, publicly accessible dataset. Our framework produces a comprehensive archive with over 24 million unique image-text pairs from over 6 million articles. Metadata and expert-guided annotations are also provided. We demonstrate the utility and accessibility of our resource by releasing BMCA-CLIP, a suite of CLIP-style models continuously pre-trained on the BIOMEDICA dataset via streaming, eliminating the need to download 27 TB of data locally. On average, our models achieve state-of-the-art performance across 40 tasks - spanning pathology, radiology, ophthalmology, dermatology, surgery, molecular biology, parasitology, and cell biology - excelling in zero-shot classification with a 6.56% average improvement (as high as 29.8% and 17.5% in dermatology and ophthalmology, respectively), and stronger image-text retrieval, all while using 10x less compute. To foster reproducibility and collaboration, we release our codebase and dataset for the broader research community.

Sahebaan Sethi

Age-related macular degeneration (AMD) is a prevalent and progressive retinal disease that affects a substantial number of elderly individuals worldwide. This manuscript provides a comprehensive overview of the current concepts in the management of AMD. The abstract begins with a brief description of the epidemiology and risk factors associated with AMD, emphasizing its increasing prevalence due to population aging. The two main subtypes of AMD, namely dry and wet, are discussed in detail, highlighting their clinical features, pathophysiology, and diagnostic techniques. The manuscript then focuses on the current management strategies for AMD. It emphasizes the significance of lifestyle modifications, including smoking cessation, healthy diet, and regular exercise, in reducing the risk and progression of AMD. Pharmacological interventions, particularly anti-vascular endothelial growth factor (anti-VEGF) agents, are extensively reviewed as the mainstay of treatment for wet AMD. The potential of emerging therapies and combination treatments is also explored. Furthermore, the manuscript addresses the role of nutritional supplements and antioxidant therapies in the management of dry AMD. It also discusses the importance of early detection and monitoring, highlighting the role of innovative imaging technologies and genetic testing in personalized treatment approaches. In conclusion, this manuscript provides a comprehensive overview of the current management strategies for AMD. By summarizing the latest advances in both pharmacological and non-pharmacological interventions, it serves as a valuable resource for clinicians, researchers, and healthcare professionals involved in the care of AMD patients. The insights presented in this manuscript contribute to the development of effective approaches in the early detection, prevention, and treatment of this visually devastating condition.

Osamu Yamada, Shih-Wei Chiu, Toru Nakazawa et al.

Abstract Background In recent years, alternative monitoring approaches, such as risk-based and remote monitoring techniques, have been recommended instead of traditional on-site monitoring to achieve more efficient monitoring. Remote risk-based monitoring (R2BM) is a monitoring technique that combines risk-based and remote monitoring and focuses on the detection of critical data and process errors. Direct data capture (DDC), which directly collects electronic source data, can facilitate R2BM by minimizing the extent of source documents that must be reviewed and reducing the additional workload on R2BM. In this study, we evaluated the effectiveness of R2BM and the synergistic effect of combining R2BM with DDC. Methods R2BM was prospectively conducted with eight participants in a randomized clinical trial using a remote monitoring system that uploaded photographs of source documents to a cloud location. Critical data and processes were verified by R2BM, and later, all were confirmed by on-site monitoring to evaluate the ability of R2BM to detect critical data and process errors and the workload of uploading photographs for clinical trial staff. In addition, the reduction of the number of uploaded photographs was evaluated by assuming that the DDC was introduced for data collection. Results Of the 4645 data points, 20.9% (n = 973, 95% confidence interval = 19.8–22.2) were identified as critical. All critical data errors corresponding to 5.4% (n = 53/973, 95% confidence interval = 4.1–7.1) of the critical data and critical process errors were detectable by R2BM. The mean number of uploaded photographs and the mean time to upload them per visit per participant were 34.4 ± 11.9 and 26.5 ± 11.8 min (mean ± standard deviation), respectively. When assuming that DDC was introduced for data collection, 45.0% (95% confidence interval = 42.2–47.9) of uploaded photographs for R2BM were reduced. Conclusions R2BM can detect 100% of the critical data and process errors without on-site monitoring. Combining R2BM with DDC reduces the workload of R2BM and further improves its efficiency.

Wei Liu, Yan Xu, Yi Wang et al.

Background Over 50% of patients with relapsed or refractory large B-cell lymphoma (r/r LBCL) receiving CD19-targeted chimeric antigen receptor (CAR19) T-cell therapy fail to achieve durable remission. Early identification of relapse or progression remains a significant challenge. In this study, we prospectively investigate the prognostic value of dynamic circulating tumor DNA (ctDNA) and track genetic evolution non-invasively, for the first time in an Asian population of r/r patients undergoing CAR19 T-cell therapy.Methods Longitudinal plasma samples were prospectively collected both before lymphodepletion and at multiple timepoints after CAR19 T-cell infusion. ctDNA was detected using a capture-based next-generation sequencing which has been validated in untreated LBCL.Results The study enrolled 23 patients with r/r LBCL and collected a total of 101 ctDNA samples. Higher pretreatment ctDNA levels were associated with inferior progression-free survival (PFS) (p=0.031) and overall survival (OS) (p=0.023). Patients with undetectable ctDNA negative (ctDNA–) at day 14 (D14) achieved an impressive 3-month complete response rate of 77.8% vs 22.2% (p=0.015) in patients with detectable ctDNA positive (ctDNA+), similar results observed for D28. CtDNA– at D28 predicted significantly longer 1-year PFS (90.9% vs 27.3%; p=0.004) and OS (90.9% vs 49.1%; p=0.003) compared with patients who remained ctDNA+. Notably, it is the first time to report that shorter ctDNA fragments (<170 base pairs) were significantly associated with poorer PFS (p=0.031 for D14; p=0.002 for D28) and OS (p=0.013 for D14; p=0.008 for D28) in patients with LBCL receiving CAR T-cell therapy. Multiple mutated genes exhibited an elevated prevalence among patients with progressive disease, including TP53, IGLL5, PIM1, BTG1, CD79B, GNA13, and P2RY8. Notably, we observed a significant correlation between IGLL5 mutation and inferior PFS (p=0.008) and OS (p=0.014).Conclusions Our study highlights that dynamic ctDNA monitoring during CAR T-cell therapy can be a promising non-invasive method for early predicting treatment response and survival outcomes. Additionally, the ctDNA mutational profile provides novel insights into the mechanisms of tumor-intrinsic resistance to CAR19 T-cell therapy.

Ishita Bajaj, Jolly Rohatgi, Rajender Prasad

Orbital cellulitis is a rare cause of exudative retinal detachment (ERD). We hereby present a case of orbital cellulitis associated with ERD from North India. A 42-year-old man presented with features of orbital cellulitis in the left eye for 3 days. An ERD lesion was found in the superior-temporal quadrant of the retina of the same eye. Investigations were performed; however, the cause of orbital cellulitis could not be found. Systemic antibiotics were started, leading to improved orbital cellulitis and resolution of ERD. Orbital cellulitis can be treated with systemic antibiotics. The cure of underlying disease leads to the resolution of ERD.

Krisztina Valter, Krisztina Valter, Stephanie E. Tedford et al.

Photobiomodulation (PBM) refers to the process in which wavelengths of light are absorbed by intracellular photoacceptors, resulting in the activation of signaling pathways that culminate in biological changes within the cell. PBM is the result of low-intensity light-induced reactions in the cell in contrast to thermal photoablation produced by high-intensity lasers. PBM has been effectively used in the clinic to enhance wound healing and mitigate pain and inflammation in musculoskeletal conditions, sports injury, and dental applications for many decades. In the past 20 years, experimental evidence has shown the benefit of PBM in increasing numbers of retinal and ophthalmic conditions. More recently, preclinical findings in ocular models have been translated to the clinic with promising results. This review discusses the preclinical and clinical evidence of the effects of PBM in ophthalmology and provides recommendations of the clinical use of PBM in the management of ocular conditions.

Danli Shi, Weiyi Zhang, Xiaolan Chen et al.

Artificial intelligence (AI) is vital in ophthalmology, tackling tasks like diagnosis, classification, and visual question answering (VQA). However, existing AI models in this domain often require extensive annotation and are task-specific, limiting their clinical utility. While recent developments have brought about foundation models for ophthalmology, they are limited by the need to train separate weights for each imaging modality, preventing a comprehensive representation of multi-modal features. This highlights the need for versatile foundation models capable of handling various tasks and modalities in ophthalmology. To address this gap, we present EyeFound, a multimodal foundation model for ophthalmic images. Unlike existing models, EyeFound learns generalizable representations from unlabeled multimodal retinal images, enabling efficient model adaptation across multiple applications. Trained on 2.78 million images from 227 hospitals across 11 ophthalmic modalities, EyeFound facilitates generalist representations and diverse multimodal downstream tasks, even for detecting challenging rare diseases. It outperforms previous work RETFound in diagnosing eye diseases, predicting systemic disease incidents, and zero-shot multimodal VQA. EyeFound provides a generalizable solution to improve model performance and lessen the annotation burden on experts, facilitating widespread clinical AI applications for retinal imaging.

Laiyi Fu, Binbin Fan, Hongkai Du et al.

Ophthalmology consultations are crucial for diagnosing, treating, and preventing eye diseases. However, the growing demand for consultations exceeds the availability of ophthalmologists. By leveraging large pre-trained language models, we can design effective dialogues for specific scenarios, aiding in consultations. Traditional fine-tuning strategies for question-answering tasks are impractical due to increasing model size and often ignoring patient-doctor role function during consultations. In this paper, we propose EyeDoctor, an ophthalmic medical questioning large language model that enhances accuracy through doctor-patient role perception guided and an augmented knowledge base with external disease information. Experimental results show EyeDoctor achieves higher question-answering precision in ophthalmology consultations. Notably, EyeDoctor demonstrated a 7.25% improvement in Rouge-1 scores and a 10.16% improvement in F1 scores on multi-round datasets compared to second best model ChatGPT, highlighting the importance of doctor-patient role differentiation and dynamic knowledge base expansion for intelligent medical consultations. EyeDoc also serves as a free available web based service and souce code is available at https://github.com/sperfu/EyeDoc.

Semp DA, Beeson D, Sheppard AL et al.

David A Semp, Danielle Beeson, Amy L Sheppard, Debarun Dutta, James S Wolffsohn School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UKCorrespondence: James S Wolffsohn, Tel +44 121 2044140, Email j.s.w.wolffsohn@aston.ac.ukAbstract: Artificial tears are the mainstay of dry eye disease management, but also have a role in corneal abrasion and wound healing, pain and inflammation management, conjunctivitis, keratitis, contact lens rewetting and removal, and foreign body removal. A systematic review of randomized controlled trials (PROSPERO registration CRD42022369619) comparing the efficacy of artificial tears in patients with dry eye to inform prescribing choices using Web of Science, PubMed and Medline databases identified 64 relevant articles. There is good evidence that artificial tears improve symptoms of dry eye disease within a month of regular use, applied about four times a day, but signs generally take several months to improve. Not all patients with dry eye disease benefit from artificial tears, so if there is no benefit over a month, alternative management should be considered. Combination formulations are more effective than single active ingredient artificial tears. Artificial tears containing polyethylene glycol are more effective than those containing carboxymethylcellulose/carmellose sodium and hydroxypropyl methylcellulose. Those classified as having evaporative dry eye disease, benefit from artificial tears with liposomes, especially of higher concentration. The data available is limited by the definition of dry eye disease applied in published studies being variable, as well as the disease severity examined and compliance with artificial tears being rarely quantified.Keywords: artificial tears, dry eye, comfort, contact lenses

Martin Röhlig, Lars Nonnemann, Hans-Jörg Schulz et al.

The visual analysis of retinal data contributes to the understanding of a wide range of eye diseases. For the evaluation of cross-sectional studies, ophthalmologists rely on workflows and toolsets established in their work environment. That is, they know what tools and data are needed at each step of their workflow. Yet, manually operating the various tools, including activation, data handling, or view arrangement, can be cumbersome and time-consuming. We thus introduce a new visualization-supported toolchaining approach that combines workflow, tools, and data. First, we provide access to the tools required for each step of the workflow. Second, we handle the exchange of data between these tools. Third, we organize the views of the tools on screen using suitable layouts. Fourth, we visualize the connection between workflow, tools, and data to support the data analysis. We demonstrate our approach with a use case in ophthalmic research and report on initial feedback from experts.

Muriel Pardon-Labonnelie, Magalie Cavé, Aurélie Troublard

A new eyedrop cachet was discovered during the excavation of the aquatic complex of Reims by Inrap in 2018. It presents exceptional morphological and epigraphic features. Like the majority of the other 365 eyedrop cachets recorded to date, this one is a rectangular parallelepiped with a square cross-section, cut from shiny grey-green rock –most likely fine-grained greywacke– the edges of which have been chamfered on the two largest sides. Its texts are particularly interesting in regards to the history of medicine. Firstly, the use of the following terms for eye drops: diamys(us), croco(des) and euodes, confirm the inclusion of Roman medicine in the Greek therapeutic tradition. Secondly, these inscriptions also reveal the name of a new practitioner: Lucius Iulius Verus. Finally, the inscriptions also include a new term for eye drops: uamomatum. The reuse of the chamfered edges for the imprint of a new practitioner’s name is, to this day, without equivalent. The discovery of Lucius Iulius Verus’ eyedrop cachet confirms the incomparable role of Reims/Durocortorum in the history of Greco-Roman ophthalmology. To date, this eye-drop cachet is the fifteenth to be unearthed from the subsoil of Reims and was found less than 300 m from the C(aius) Censori(us) Verus stone. If this placement is not fortuitous, two practitioners bearing the same cognomen is a coincidence attesting to a professional filiation that is all the more fascinating, as it is entirely unique.

Chantarasorn Y, Rasmidatta K, Pokawattana I et al.

Yodpong Chantarasorn,1 Kochapong Rasmidatta,1 Itsara Pokawattana,1,2 Sukhum Silpa-archa3 1Department of Ophthalmology, Vajira Hospital, Navamindradhiraj University, Bangkok, 10300, Thailand; 2Department of Ophthalmology, H.R.H Maha Chakri Sirindhorn Medical Center, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; 3Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, 10400, ThailandCorrespondence: Yodpong Chantarasorn, Vajira Hospital, Navamindradhiraj University, 681 Samsen Street, Bangkok, 10300, Thailand, Tel +66 (89) 103-6633, Fax +66 (2) 241-4388, Email yodpong@nmu.ac.thPurpose: Patients with hypercortisolism have been associated with a higher prevalence of the pachychoroid spectrum including central serous chorioretinopathy (CSCR), which may explain the inconsistency of therapeutic responses of the mineralocorticoid receptor antagonist because hyperaldosteronism has rarely been detected in patients with CSCR. Therefore, this study aimed to evaluate the effects of ketoconazole, the first-line cortisol inhibitor, on the resolution of subretinal fluid (SRF) in CSCR and to analyze correlations between choroidal thickness and steroid hormones.Patients and Methods: This retrospective cohort study included 41 naïve CSCR eyes of 41 patients categorized into control (20 eyes) and treatment (21 eyes) groups. Patients in the treatment group were administered oral ketoconazole at a daily dose of 400 or 600 mg for 3– 6 weeks. At week 12, rescue laser therapy was applied to patients exhibiting persistent SRF. Thus, a survival analysis was performed to determine the time interval from presentation to clinical resolution of SRF. Secondary outcomes consisted of eyes with persistent SRF and factors affecting the therapeutic response.Results: The mean 24-hour urinary free cortisol (UFC) levels were elevated at 181 ± 70 and 150 ± 68 μg/day (range: 20– 150) in the treatment and control groups, respectively (p = 0.21). After controlling for age and gender, baseline UFC levels were significantly associated with choroidal thickness in both eyes (p < 0.05). Ketoconazole significantly increased the CSCR resolution with the median time to resolution of 7 vs 16 weeks (p < 0.01) and decreased the proportion of eyes receiving rescue therapy at 12 weeks (23.8% vs 50%; p = 0.01). Prolonged CSCR durations were likely found in elderly patients with thick choroids in fellow eyes.Conclusion: Patients with CSCR showed elevated glucocorticoids, which further correlated with their choroidal thickness. Using cortisol blockers may shorten the duration of existing SRF.Keywords: pachychoroidopathy, cortisol, choroidal thickness, mineralocorticoid receptor antagonist

Shettle L, McLaurin E, Martel J et al.

Lee Shettle,1 Eugene McLaurin,2 Joseph Martel,3 John W Seaman III,4 Georges Weissgerber5 1Shettle Eye Research, Inc, Largo, FL, USA; 2Total Eye Care, Memphis, TN, USA; 3Research Center, Martel Eye Medical Group, Rancho Cordova, CA, USA; 4Novartis Pharmaceuticals Corporation, Fort Worth, TX, USA; 5Novartis Institutes for Biological Research, Basel, SwitzerlandCorrespondence: Lee Shettle, Shettle Eye Research, Inc, Largo, FL, USA, Tel +1 727-674-2500, Fax +1 727-674-2550, Email lshettle@yahoo.com Eugene McLaurin, Total Eye Care, Memphis, TN, USA, Tel +1-912-441-2128, Email emclaurin6@aol.comPurpose: To assess the efficacy, safety, and pharmacokinetics of new topical ocular anti-TNF&alpha; antibody fragment licaminlimab in the relief of persistent ocular discomfort in severe dry eye disease (DED).Patients and Methods: Patients with &ge; 6-month history of DED, regular use of artificial tears, and best-corrected visual acuity (BCVA) of &ge; 55 letters in each eye (Early Treatment Diabetic Retinopathy Score) at baseline were included in this multicenter, randomized, vehicle-controlled, double masked study. A total of 514 patients were screened. After a 2-week run-in with Vehicle, all qualifying patients received Vehicle eye drops for 4 weeks. Patients with global ocular discomfort score &ge; 50 at the end of this 4-week period were randomized to receive licaminlimab (60 mg/mL ophthalmic solution) (69 patients) or Vehicle (65 patients) for 6 weeks. The primary efficacy endpoint was change from baseline in global ocular discomfort score at Day 29. Safety assessments included adverse events and ophthalmology examination including intraocular pressure (IOP). Serum licaminlimab levels were also determined.Results: Change from baseline to Day 29 in global ocular discomfort score was statistically significantly greater for licaminlimab than for Vehicle (p = 0.041). No safety issues were identified. Serum licaminlimab was undetectable in most patients; the maximum concentration observed was 8.47 ng/mL.Conclusion: Topical ocular licaminlimab demonstrated statistically significant improvement in global ocular discomfort score compared to Vehicle in patients with severe DED, with good tolerability, no increase in IOP, and minimal systemic drug exposure.Keywords: anti-tumor necrosis factor &alpha;, dry eye disease, single-chain antibody fragment, topical treatment