[OPHTHALMOLOGY - ON THE TIP OF THE SPEAR].

INTRODUCTION: Ophthalmology is a broad branch of medicine, which includes an extensive range of sub-specialties on one hand, and interfaces with other fields of medicine on the other. This issue contains papers from different sub-specialties of ophthalmology, that together cover several of the most important issues in this field. These papers present the topics in a manner compatible with the wide readership of the journal, and touch upon the most current updates and innovations. The original articles in this issue deal with treatments for the prevention of myopia progression in children, treatment of complicated cases of retinal detachment in children, ocular manifestations of vascular abnormalities in patients with coronavirus, and a series of patients with corneal damage due to ultraviolet-C (UVC) lamps intended to clear the air of this virus. The review papers describe glaucoma and the current change in its treatment paradigm, which focuses on earlier intervention, ocular manifestations of systemic autoimmune diseases, and the possibilities for artificial corneal implantation. We hope that this special issue will be of interest and clinical value to its readers.

"Spaceflight-to-Eye Clinic": Terrestrial advances in ophthalmic healthcare delivery from space-based innovations.

The phrase "Bench-to-Bedside" is a well-known phrase in medicine, highlighting scientific discoveries that directly translate to impacting patient care. Key examples of translational research include identification of key molecular targets in diseases and development of diagnostic laboratory tests for earlier disease detection. Bridging these scientific advances to the bedside/clinic has played a meaningful impact in numerous patient lives. The spaceflight environment poses a unique opportunity to also make this impact; the nature of harsh extraterrestrial conditions and medically austere and remote environments push for cutting-edge technology innovation. Many of these novel technologies built for the spaceflight environment also have numerous benefits for human health on Earth. In this manuscript, we focus on "Spaceflight-to-Eye Clinic" and discuss technologies built for the spaceflight environment that eventually helped to optimize ophthalmic health on Earth (e.g., LADAR for satellite docking now utilized in eye-tracking technology for LASIK). We also discuss current technology research for spaceflight associated neuro-ocular syndrome (SANS) that may also be applied to terrestrial ophthalmic health. Ultimately, various advances made to enable to the future of space exploration have also advanced the ophthalmic health of individuals on Earth.

Converting LEA to Pelli-Robson contrast sensitivity score in neuro-ophthalmology patients.

PURPOSE: The Pelli-Robson and LEA contrast sensitivity charts are commonly used in clinical settings to measure contrast sensitivity. Although the Pelli-Robson chart is considered the gold standard, it is limited by its bulky size. The LEA chart, on the contrary, offers a more practical and portable option that is still reliable. This has led to questions about whether we can predict Pelli-Robson scores based on LEA scores. This study developed a conversion method to help transition from the LEA chart to the Pelli-Robson chart and validate the conversion score. METHODS: In this retrospective study, we analyzed the relationship between LEA and the Pelli-Robson contrast sensitivity test. Our study examined a total of 120 eyes. We developed a conversion table through the equipercentile equating method. Subsequently, we assessed the reliability and accuracy of this algorithm for converting LEA results into Pelli-Robson contrast sensitivity scores. RESULTS: The study used a conversion table to convert LEA scores to Pelli-Robson scores. The conversion table achieved a reliability of 0.91 based on intraclass correlation, and the algorithm had an accuracy of 81.6% within a 1-point difference from the raw score. CONCLUSIONS: This study reported a reliable and comparable conversion algorithm for transforming LEA scores into converted estimated Pelli-Robson scores, thereby improving the usefulness of existing data in both clinical and research contexts.

Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs.

Artificial intelligence (AI) has great potential in ophthalmology. We investigated how ambiguous outputs from an AI diagnostic support system (AI-DSS) affected diagnostic responses from optometrists when assessing cases of suspected retinal disease. Thirty optometrists (15 more experienced, 15 less) assessed 30 clinical cases. For ten, participants saw an optical coherence tomography (OCT) scan, basic clinical information and retinal photography ('no AI'). For another ten, they were also given AI-generated OCT-based probabilistic diagnoses ('AI diagnosis'); and for ten, both AI-diagnosis and AI-generated OCT segmentations ('AI diagnosis + segmentation') were provided. Cases were matched across the three types of presentation and were selected to include 40% ambiguous and 20% incorrect AI outputs. Optometrist diagnostic agreement with the predefined reference standard was lowest for 'AI diagnosis + segmentation' (204/300, 68%) compared to 'AI diagnosis' (224/300, 75% p = 0.010), and 'no Al' (242/300, 81%, p = < 0.001). Agreement with AI diagnosis consistent with the reference standard decreased (174/210 vs 199/210, p = 0.003), but participants trusted the AI more (p = 0.029) with segmentations. Practitioner experience did not affect diagnostic responses (p = 0.24). More experienced participants were more confident (p = 0.012) and trusted the AI less (p = 0.038). Our findings also highlight issues around reference standard definition.

The Current State of Artificial Intelligence in Neuro-Ophthalmology. A Review

This article presents a summary of recent advances in the development and use of complex systems using artificial intelligence (AI) in neuro-ophthalmology. The aim of the following article is to present the principles of AI and algorithms that are currently being used or are still in the stage of evaluation or validation within the neuro-ophthalmology environment. For the purpose of this text, a literature search was conducted using specific keywords in available scientific databases, cumulatively up to April 2023. The AI systems developed across neuro-ophthalmology mostly achieve high sensitivity, specificity and accuracy. Individual AI systems and algorithms are subsequently selected, simply described and compared in the article. The results of the individual studies differ significantly, depending on the chosen methodology, the set goals, the size of the test, evaluated set, and the evaluated parameters. It has been demonstrated that the evaluation of various diseases will be greatly speeded up with the help of AI and make the diagnosis more efficient in the future, thus showing a high potential to be a useful tool in clinical practice even with a significant increase in the number of patients.

Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review.

BACKGROUND: Imaging plays a pivotal role in eye assessment. With the introduction of advanced machine learning and artificial intelligence (AI), the focus has shifted to imaging datasets in ophthalmology. While disparities and health inequalities hidden within data are well-documented, the ophthalmology field faces specific challenges to the creation and maintenance of datasets. Optical Coherence Tomography (OCT) is useful for the diagnosis and monitoring of retinal pathologies, making it valuable for AI applications. This review aims to identify and compare the landscape of publicly available optical coherence tomography databases for AI applications. METHODS: We conducted a literature review on OCT and AI articles with publicly accessible datasets, using PubMed, Scopus, and Web of Science databases. The review retrieved 183 articles, and after full-text analysis, 50 articles were included. From the included articles were identified 8 publicly available OCT datasets, focusing on patient demographics and clinical details for thorough assessment and comparison. RESULTS: The resulting datasets encompass 154,313 images collected from Spectralis, Cirrus HD, Topcon 3D, and Bioptigen devices. These datasets included normal exams, age-related macular degeneration, and diabetic maculopathy, among others. Comprehensive demographic information is available in one dataset and the USA is the most represented population. DISCUSSION: Current publicly available OCT databases for AI applications exhibit limitations, stemming from their non-representative nature and the lack of comprehensive demographic information. Limited datasets hamper research and equitable AI development. To promote equitable AI algorithmic development in ophthalmology, there is a need for the creation and dissemination of more representative datasets.

Visualization Materials Using Silicon-Based Optical Nanodisks (ViSiON) for Enhanced NIR Imaging in Ophthalmology.

ViSiON (visualization materials composed of silicon-based optical nanodisks) is presented, which offers a unique optical combination of near-infrared (NIR) optical properties and biodegradability. Initially, numerical simulations are conducted to calculate the total extinction and scattering effects of ViSiON by the diameter-to-thickness ratio, predicting precise control over its scattering properties in the NIR region. A top-down patterning technique is employed to synthesize ViSiON with accurate diameter and thickness control. ViSiON with a 50 nm thickness exhibits scattering properties over 400 times higher than that of 30 nm, rendering it suitable as a contrast agent for optical coherence tomography (OCT), especially in ophthalmic applications. Furthermore, ViSiON possesses inherent biodegradability in media, with ≈95% degradation occurring after 48 h, and the degradation rate can be finely tuned based on the quantity of protein coating applied to the surface. Subsequently, the OCT imaging capability is validated even within vessels smaller than 300 µm, simulating retinal vasculature using a retinal phantom. Then, using an ex ovo chick embryo model, it is demonstrated that ViSiON enhances the strength of protein membranes by 6.17 times, thereby presenting the potential for ViSiON as an OCT imaging probe capable of diagnosing retinal diseases.

Recent developments of telemedicine in glaucoma.

PURPOSE OF REVIEW: Telemedicine has an increasingly significant role in the fields of ophthalmology and glaucoma. This review covers recent advancements in the development and optimization of teleglaucoma techniques and applications. RECENT FINDINGS: Glaucoma monitoring and diagnosis via remote tonometry, perimetry, and fundus imaging have become a possibility based on recent developments. Many applications work in combination with smart devices, virtual reality, and artificial intelligence and have been tested in patient populations against conventional "reference-standard" measurement tools, demonstrating promising results. Of note, there is still much progress to be made in teleglaucoma and telemedicine at large, such as accessibility to internet, broadband, and smart devices, application affordability, and reimbursement for remote services. However, continued development and optimization of these applications suggest that the implementation of remote monitoring will be a mainstay for glaucoma patient care. SUMMARY: Especially since the beginning of the COVID-19 pandemic, remote patient care has taken on an important role in medicine and ophthalmology. Remote versions of tonometry, perimetry, and fundus imaging may allow for a more patient-centered and accessible future for glaucoma care.

Evaluating patient flow through an emergency ophthalmology consult service in a tertiary care academic centre in Quebec.

OBJECTIVE: This study aims to characterize the epidemiology, common reasons, and diagnostic accuracy of referrals made by emergency departments (EDs) and optometrists to an emergency ophthalmology consultation service as well as to identify opportunities for improvement. DESIGN: Retrospective chart review. PARTICIPANTS: A total of 1249 referrals made to the emergency ophthalmology consultation service at a tertiary care centre between July 2018 and June 2019. METHODS: Patient charts were examined, and clinical variables were extracted. Statistical significance (p < 0.05) was determined via t tests and χ2 tests for continuous and categorical variables, respectively. Diagnostic accuracy of providers and time delay between referral and ophthalmologic encounter also were assessed. RESULTS: Both EDs and optometrists most often referred cases with vitreoretinal (36.48% and 37.19%, respectively) and corneal pathology (21.42% and 20.25%, respectively). Optometrists (n = 240; 52.48%) were significantly more accurate in their diagnoses than EDs (n = 940; 32.45%; p < 0.00001). Specifically, optometrists were significantly more accurate when diagnosing anterior-chamber (n = 29; 58.62%; p = 0.039) and vitreoretinal (n = 89; 60.67%; p < 0.00001) pathology than EDs (anterior chamber, n = 77, 36.36%; vitreoretinal, n = 344, 18.90%). Across all ED referrals (n = 940), 58 (6.17%) had a prolonged delay. Across all optometrist-to-ED referrals (n = 150), 6 (4.00%) had a prolonged delay. Accounting for all cases, the total incidence of prolonged delay was 5.87%. CONCLUSIONS: Our results demonstrate the need for improved communication between optometrists and ophthalmologists to reduce the wait-time burden on EDs. Patients may benefit from direct referral by optometrists to ophthalmologists. Education of allied health professionals on ophthalmic disease also may improve diagnostic accuracy.

Manhattan Vision Screening and Follow-Up Study (NYC-SIGHT): Subanalysis of Referral to Ophthalmology.

PURPOSE: The Manhattan Vision Screening and Follow-up Study aims to provide access to eye care for underserved populations, detect native rates of ocular pathology, and refer participants with eye disease to ophthalmology. This subanalysis describes the reasons for referral to ophthalmology and identifies risk factors associated with being referred. METHODS: Enrolled participants were aged ≥40 years, living independently in public housing developments and able to provide consent for eye health screenings. Those with habitual visual acuity 20/40 or worse, intraocular pressure (IOP) 23-29 mmHg, or an unreadable fundus image failed and were scheduled with the on-site optometrist. The optometric exam determined whether further referral to ophthalmology for a clinic exam was warranted. Those with an abnormal image or IOP ≥30 mmHg were referred directly to ophthalmology. Main outcome was factors associated with referral to ophthalmology. RESULTS: A total of 708 individuals completed the eye health screening over 15 months. A total of 468 participants were referred to ophthalmology (250 had an abnormal image and 218 were referred by the optometrist). Those referred were predominantly older adults (mean age 70.0 ± 11.4 years), female (66.7%), African American (55.1%) and Hispanic (39.5%). Seventy percent of participants had not had a recent eye exam. Stepwise multivariate logistic regression analysis showed that participants with pre-existing glaucoma (OR 3.14, 95% CI 1.62 to 6.08, p = 0.001), an IOP ≥23 mmHg (OR 5.04, 95% 1.91 to 13.28, p = 0.001), or vision impairment (mild) (OR 2.51, 95% CI 1.68 to 3.77, p = 0.001) had significantly higher odds of being referred to ophthalmology. CONCLUSION: This targeted community-based study in Upper Manhattan provided access to eye care and detected a significant amount of ocular pathology requiring referral to ophthalmology in this high-risk population.

Detecting Eye Disease Using Vision Transformers Informed by Ophthalmology Resident Gaze Data.

We showcase two proof-of-concept approaches for enhancing the Vision Transformer (ViT) model by integrating ophthalmology resident gaze data into its training. The resulting Fixation-Order-Informed ViT and Ophthalmologist-Gaze-Augmented ViT show greater accuracy and computational efficiency than ViT for detection of the eye disease, glaucoma.Clinical relevance- By enhancing glaucoma detection via our gaze-informed ViTs, we introduce a new paradigm for medical experts to directly interface with medical AI, leading the way for more accurate and interpretable AI 'teammates' in the ophthalmic clinic.

Artificial intelligence in glaucoma: opportunities, challenges, and future directions.

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Potency of teleophthalmology as a detection tool for diabetic retinopathy.

In China, the prevalence of diabetic retinopathy (DR) is increasing, so it is necessary to provide convenient and effective community outreach screening programs for DR, especially in rural and remote areas. The purpose of this study was to use the results of ophthalmologists as the gold standard to evaluate the accuracy of community general practitioners' judgement and grading of DR to find a feasible and convenient DR screening method to reduce the risk of visual impairment and blindness in known diabetes patients. Retinal images of 1646 diabetic patients who underwent DR screening through teleophthalmology at Nanchang First Hospital were collected for 30 months (January 2020 to June 2022). Retinal images were collected without medication for mydriasis, stored by community general practitioner, and diagnosed by both community general practitioner and ophthalmologist of our hospital through teleophthalmology. The grading of ophthalmologist was used as a reference or gold standard for comparison with that of community general practitioner. A total of 1646 patients and 3185 eyes were examined, including 2310 eyes with DR. The evaluation by the community general practitioner had a Kappa value of 0.578, sensitivity of 80.58%, specificity of 89.94%, and accuracy of 83.38%% in 2020; a Kappa value of 0.685, sensitivity of 95.43%, specificity of 78.55%, and accuracy of 90.77% in 2021; and a Kappa value of 0.744, sensitivity of 93.99%, specificity of 88.97%, and accuracy of 92.86% in 2022. Teleophthalmology helped with large-scale screening of DR and made it possible for community general practitioner to grade images with high accuracy after appropriate training. It is possible to solve the current shortage of eye care personnel, promote the early recognition of disease and reduce the impact of diabetes-associated blindness.

Artificial intelligence within ophthalmologic diseases.

As ophthalmology is an increasingly busy medical specialty relying solidly on imaging technology, this review investigates the introduction of artificial intelligence to improve diagnostic performance and reduce human resources. In diabetic retinopathy screening, algorithms are now regulatory-approved for international markets but not yet tailored for the Danish system. In age-related macular degeneration, algorithms are now able to facilitate the classification and segmentation of disease activity, and in upcoming years, these are likely to assist us to improve diagnosis and provide subsequent clinical care.

The Current State of Artificial Intelligence in Neuro-Ophthalmology. A Review.

This article presents a  summary of recent advances in the development and use of complex systems using artificial intelligence (AI) in neuroophthalmology. The aim of the following article is to present the principles of AI and algorithms that are currently being used or are still in the stage of evaluation or validation within the neuro-ophthalmology environment. For the purpose of this text, a literature search was conducted using specific keywords in available scientific databases, cumulatively up to April 2023. The AI systems developed across neuro-ophthalmology mostly achieve high sensitivity, specificity and accuracy. Individual AI systems and algorithms are subsequently selected, simply described and compared in the article. The results of the individual studies differ significantly, depending on the chosen methodology, the set goals, the size of the test, evaluated set, and the evaluated parameters. It has been demonstrated that the evaluation of various diseases will be greatly speeded up with the help of AI and make the diagnosis more efficient in the future, thus showing a high potential to be a useful tool in clinical practice even with a significant increase in the number of patients.

[Chinese expert consensus on telemedicine for diabetic retinopathy (2023)].

The practice of telemedicine for diabetic retinopathy (DR) is an important measure to integrate the advantages of multi-level medical and health institutions and ensure quality medical services and safe treatment. According to both the clinical experience in preventive medicine and ophthalmology and the domestic and foreign guidelines, the experts of the Public Health Ophthalmology Branch of Chinese Preventive Medicine Association have developed the consensus opinions on the requirements of operating systems, quality requirements of fundus images, diagnostic criteria, recommendation and referral standards, and management objectives of DR telemedicine. The formulation of this consensus will help to improve the screening and diagnosis capacity of primary medical institutions for DR and contribute to the development of Healthy China.

Artificial intelligence in ophthalmology: The path to the real-world clinic.

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.