Artificial intelligence holds the potential to transform ophthalmic assessment, yet its translation into clinical practice remains hindered by its limited ability to perform complex reasoning, utilize specialized tools, or provide transparent justifications. We introduce OphAgent, a multi-agent framework powered by large language models that mimics clinical workflows through dynamic diagnostic planning, knowledge base consultation, and multimodal image interpretation via structured inter-agent debate. Validated across four imaging modalities, 30+ conditions, and 11 diverse real-world datasets, OphAgent achieved 85% average accuracy in retinal diagnosis, surpassing current state-of-the-art multimodal large language models. In a large-scale global reader study with 311 ophthalmologists from 86 centres across 22 countries, spanning seven languages and settings from tertiary hospitals to resource-limited clinics, OphAgent improved diagnostic accuracy by up to 20% across 14 clinically significant tasks. Critically, these gains remained stable across geographic regions, experience levels, and languages, demonstrating robustness, fairness, and generalisability. OphAgent exemplifies an emerging paradigm where medical AI evolves from isolated prediction to collaborative clinical reasoning. By integrating reasoned clinical consultation with state-of-the-art diagnostic performance, the framework offers a scalable pathway toward more equitable, transparent, and trustworthy AI-enabled clinical decision support, with the potential to extend expert-level ophthalmic assessment across diverse healthcare settings.

Abstract Are humans and humanity the only categories for the understanding of language(s) and education? Do we need to think otherwise? Shouldn’t plurality, difference and otherness be indispensable categories for us - Sapiens - to rethink the humanism imbued in Modern concepts of language and education? These are questions I intend to tackle in this text. The first part of this article focuses on two main concepts of language and revisits structuralist, post-structuralist and post-humanist views of language, claiming that the latter two are sine qua non conditions for understanding language education. The second section investigates the concepts of ‘life’ and institutional education as vehicles of Modernity and shows that liberal/bildung education has led us to rather individualist, self-proclaimed, and self-emancipated views of the world. Finally, I define language education by revisiting the concept of teaching and learning of languages and claim for a post-humanist transdisciplinary language education.

This article investigates the visual strategies employed by Palestinian citizens amid the ongoing Israeli-Palestinian conflict, focusing on how Instagram is used to challenge hegemonic media narratives that predominantly highlight violent hostage liberation scenes. As modern and humanist epistemologies increasingly reimagine Gaza/conflict zone as a commodified real estate venture and resort, this study examines how visual language becomes a tool for resisting colonization and oppression. Drawing on Southern epistemologies (Sousa Santos, 2018), the analysis highlights locally rooted visual semiotics that convey the lived experiences of Gazans. Through visual digital ethnography and visual literacy (Mizan; Ferraz, 2021), the study reveals how these audiovisual materials foster a decolonial perspective, subverting Western media representations and amplifying the voices of marginalized communities. This research ultimately contributes to the Southernization of Applied Linguistics by foregrounding alternative visual discourses that challenge colonial and capitalist commodification of the region and reassert local narratives of resistance.

ABSTRACT This article seeks to revisit the place of genders and sexualities in language education. It emphasizes the importance of addressing the languages of marginalized non-hegemonic bodies, including non-heterosexual/heteronormative bodies, bodies who continue to face abjection and exclusion in contemporary societies. It investigates the (im)possible and (im)probable encounter amongst language education and pedagogies focused on difference, sexualities, genders and their intersections with visual literacies. The research adopts a qualitative methodology and the methods of investigation involve review of literature, alongside digital document analysis through visual literacy methodologies in which images on social media platforms are interpreted as meaning making processes. Section 1 discusses the concepts of genders and sexualities by focusing on nonheteronormative and non-heterosexual existences. Section 2 contends that language education, visual literacies and gender and sexuality studies could and should be in constant dialogue in language classes. Finally, the article offers four praxeologies for language teacher education amidst the intersections of sexualities, and genders.

RESUMO: A educação formal tende a separar o processo educacional das demais esferas da vida humana. Nessa perspectiva, as escolas muitas vezes ficam desconectadas da realidade vivenciada pelos estudantes. Além disso, as abordagens do ensino tendem a promover valores típicos da sociedade neoliberal capitalista, como a competitividade e o individualismo. No entanto, uma visão crítica da educação propõe um diálogo com as experiências dos alunos. Essa abordagem busca não apenas integrar o conhecimento formal com a realidade dos alunos, mas também promover uma compreensão mais ampla e crítica do mundo ao seu redor. Este artigo apresenta três narrativas de professores de educação linguística em língua inglesa que empregam o letramento visual para desenvolver criticidade na sala de aula em relação às vivências e realidades dos alunos, formando desta maneira educadores capazes de refletir sobre verdades absolutas que dominam o nosso imaginário.

Western epistemological approaches in language education have traditionally constructed a celebratory discourse on technological development by promoting "hyperbolic narratives of the big data revolution" (Milan; Trere, 2019, p. 320). This dominant epistemological approach is contextual and it is designed to serve the interests of the industry, governments, and science of the geographical place in which it is framed - the Global North. In this paper, we call for an epistemological change in the positivist take on Big Data, and we challenge its seemingly universal and beneficial mindset. We seek to show that the theories on digital literacies - although considered to be critical of traditional literacies - have not touched upon the realities of digital capitalist colonization of the Global South. The article tackles digital colonization by presenting and analyzing the coloniality of knowledge, power and being in the Global South. The coloniality of knowledge manifests itself through the colonization of education and common sense in the Global South. The coloniality of power emerges in the form of digital capitalism, platform capitalism and surveillance capitalism. And finally, the coloniality of being takes the shape of digital influencers who market themselves. We seek to contest and de-Westernize the discourses on digital literacies in language education classrooms by revealing the power's opacity they create.

O livro apresenta discussões em torno dos estudos das imagens em contexto de pós-graduação. A primeira parte - Letramentos Visuais e Sociedade - problematiza o cinema, as redes sociais, as artes fotográficas e o grafite. A segunda parte – Letramentos Visuais e Educação Linguística – nos brinda com capítulos que versam sobre a educação linguística e letramentos visuais nas aulas de língua inglesa, língua espanhola e língua portuguesa. Conversamos sobre os monumentos históricos, formação docente, projetos de educação linguística para o ensino médio, EJA e para alunos portadores de TDAH.

PURPOSE:To investigate the sociodemographic profile, the association with retinal vascular diseases (RVD) and systemic comorbidities, and visual outcomes of patients with paracentral acute middle maculopathy (PAMM) in a large, ethnically diverse single-center cohort. DESIGN:Retrospective cohort study. METHODS:Electronic health record query for all patients presenting with PAMM at Moorfields Eye Hospital, London, was completed. Detailed demographic, clinical, and systemic information were collected and analyzed. RESULTS:A total of 78 eyes of 78 patients with confirmed PAMM were included in the study. Forty patients (51.3%) presented with no RVD, 20 patients (25.6%) with retinal vein occlusion (RVO), 16 patients (20.5%) with retinal artery occlusion (RAO), and 2 patients (2.6%) with concomitant RAO and RVO. Patients with PAMM+RAO were older than those with RVO (P = .02) and more likely to have a history of major adverse cardiovascular events (MACE) (P = .01), with a significantly worse presenting best corrected visual acuity (BCVA) (20/50) compared to patients with RVO (P = .02) and no RVD (P < .001). Individuals with isolated PAMM had a significantly higher prevalence of previous MACE (P = .04) and sickle cell disease (SCD) (P = .04) compared to those with RVO. At the last follow-up, 64 patients (85.3%) had a good BCVA (>20/32). CONCLUSIONS:The significant association of PAMM with RVD supports the hypothesis of an ischemic etiology. Individuals with isolated PAMM had a higher prevalence of MACE and SCD. Thus, it is important to prompt immediate referral for a comprehensive systemic evaluation. Across the whole cohort, PAMM was associated with good BCVA improvement during follow-up, indicating a good visual prognosis.

Background Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available experts has raised concerns about the sustainability of this approach. We aimed to develop bespoke and code-free deep learning-based classifiers for plus disease, a hallmark of ROP, in an ethnically diverse population in London, UK, and externally validate them in ethnically, geographically, and socioeconomically diverse populations in four countries and three continents. Code-free deep learning is not reliant on the availability of expertly trained data scientists, thus being of particular potential benefit for low resource health-care settings. Methods This retrospective cohort study used retinal images from 1370 neonates admitted to a neonatal unit at Homerton University Hospital NHS Foundation Trust, London, UK, between 2008 and 2018. Images were acquired using a Retcam Version 2 device (Natus Medical, Pleasanton, CA, USA) on all babies who were either born at less than 32 weeks gestational age or had a birthweight of less than 1501 g. Each images was graded by two junior ophthalmologists with disagreements adjudicated by a senior paediatric ophthalmologist. Bespoke and code-free deep learning models (CFDL) were developed for the discrimination of healthy, pre-plus disease, and plus disease. Performance was assessed internally on 200 images with the majority vote of three senior paediatric ophthalmologists as the reference standard. External validation was on 338 retinal images from four separate datasets from the USA, Brazil, and Egypt with images derived from Retcam and the 3nethra neo device (Forus Health, Bengaluru, India).Findings Of the 7414 retinal images in the original dataset, 6141 images were used in the final development dataset. For the discrimination of healthy versus pre-plus or plus disease, the bespoke model had an area under the curve (AUC) of 0 & BULL;986 (95% CI 0 & BULL;973-0 & BULL;996) and the CFDL model had an AUC of 0.989 (0.979-0.997) on the internal test set. Both models generalised well to external validation test sets acquired using the Retcam for discriminating healthy from pre-plus or plus disease (bespoke range was 0.975-1.000 and CFDL range was 0.969-0.995). The CFDL model was inferior to the bespoke model on discriminating pre-plus disease from healthy or plus disease in the USA dataset (CFDL 0 & BULL;808 [95% CI 0 & BULL;671-0 & BULL;909, bespoke 0 & BULL;942 [0 & BULL;892-0 & BULL;982]], p=0 & BULL;0070). Performance also reduced when tested on the 3nethra neo imaging device (CFDL 0 & BULL;865 [0 & BULL;742-0 & BULL;965] and bespoke 0 & BULL;891 [0 & BULL;783-0 & BULL;977]).Interpretation Both bespoke and CFDL models conferred similar performance to senior paediatric ophthalmologists for discriminating healthy retinal images from ones with features of pre-plus or plus disease; however, CFDL models might generalise less well when considering minority classes. Care should be taken when testing on data acquired using alternative imaging devices from that used for the development dataset. Our study justifies further validation of plus disease classifiers in ROP screening and supports a potential role for code-free approaches to help prevent blindness in vulnerable neonates.

This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it’s recommended that this information be reported in a clear, specific, and accessible way whenever possible. Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.

Telemedicine, the use of telecommunication and information technology to deliver healthcare remotely, has evolved beyond recognition since its inception in the 1970s. Advances in telecommunication infrastructure, the advent of the Internet, exponential growth in computing power and associated computer-aided diagnosis, and medical imaging developments have created an environment where telemedicine is more accessible and capable than ever before, particularly in the field of ophthalmology. Ever-increasing global demand for ophthalmic services due to population growth and ageing together with insufficient supply of ophthalmologists requires new models of healthcare provision integrating telemedicine to meet present day challenges, with the recent COVID-19 pandemic providing the catalyst for the widespread adoption and acceptance of teleophthalmology. In this review we discuss the history, present and future application of telemedicine within the field of ophthalmology, and specifically retinal disease. We consider the strengths and limitations of teleophthalmology, its role in screening, community and hospital management of retinal disease, patient and clinician attitudes, and barriers to its adoption.

PURPOSE:This study aimed to use computational models for simulating the movement of respiratory droplets when assessing the efficacy of standard slit-lamp shield versus a new shield designed for increased clinician comfort as well as adequate protection. METHODS:Simulations were performed using the commercial software Star-CCM+. Respiratory droplets were assumed to be 100% water in volume fraction with particle diameter distribution represented by a geometric mean of 74.4 (±1.5 standard deviation) μm over a 4-min duration. The total mass of respiratory droplets expelled from patients' mouths and droplet accumulation on the manikin were measured under the following three conditions: with no slit-lamp shield, using the standard slit-lamp shield, and using our new proposed shield. RESULTS:The total accumulated water droplet mass (kilogram) and percentage of expelled mass accumulated on the shield under the three aforementioned conditions were as follows: 5.84e-10 kg (28% of the total weight of particle emitted that settled on the manikin), 9.14e-13 kg (0.045%), and 3.19e-13 (0.015%), respectively. The standard shield could shield off 99.83% of the particles that would otherwise be deposited on the manikin, which is comparable to 99.95% for the proposed design. Conclusion: Slit-lamp shields are effective infection control tools against respiratory droplets. The proposed shield showed comparable effectiveness compared with conventional slit-lamp shields, but with potentially enhanced ergonomics for ophthalmologists during slit-lamp examinations.

Abstract Background To evaluate the efficacy of retinal photography obtained by undergraduate students using a smartphone-based device in screening and early diagnosing diabetic retinopathy (DR). Methods We carried out an open prospective study with ninety-nine diabetic patients (194 eyes), who were submitted to an ophthalmological examination in which undergraduate students registered images of the fundus using a smartphone-based device. At the same occasion, an experienced nurse captured fundus photographs from the same patients using a gold standard tabletop camera system (Canon CR-2 Digital Non-Mydriatic Retinal Camera), with a 45º field of view. Two distinct masked specialists evaluated both forms of imaging according to the presence or absence of sings of DR and its markers of severity. We later compared those reports to assess agreement between the two technologies. Results Concerning the presence or absence of DR, we found an agreement rate of 84.07% between reports obtained from images of the smartphone-based device and from the regular (tabletop) fundus camera; Kappa: 0.67; Sensitivity: 71.0% (Confidence Interval [CI]: 65.05–78.16%); Specificity: 94.06% (CI: 90.63–97.49%); Accuracy: 84.07%; Positive Predictive Value (PPV): 90.62%; Negative Predictive Value (NPV): 80.51%. As for the classification between proliferative diabetic retinopathy and non-proliferative diabetic retinopathy, we found an agreement of 90.00% between the reports; Kappa: 0.78; Sensitivity: 86.96%; (CI: 79.07–94.85%); Specificity: 91.49% (CI: 84.95–98.03%); Accuracy: 90.00%; PPV: 83.33%; NPV: 93.48%. Regarding the degree of classification of DR, we found an agreement rate of 69.23% between the reports; Kappa: 0.52. As relating to the presence or absence of hard macular exudates, we found an agreement of 84.07% between the reports; Kappa: 0.67; Sensitivity: 71.60% (CI: 65.05–78.16%); Specificity: 94.06% (CI: 90.63–97.49%); Accuracy: 84.07%; PPV: 90.62%; NPV: 80.51%. Conclusion The smartphone-based device showed promising accuracy in the detection of DR (84.07%), making it a potential tool in the screening and early diagnosis of DR.

BACKGROUND:Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting.METHOD:Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR.RESULTS:A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading.CONCLUSIONS:The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.

PURPOSE:To evaluate contrast sensitivity in non-high-risk, treatment-naïve proliferative diabetic retinopathy patients treated with panretinal photocoagulation and intravitreal injections of ranibizumab) versus panretinal photocoagulation alone. METHODS:Sixty eyes of 30 patients with bilateral proliferative diabetic retinopathy were randomized into two groups: one received panretinal photocoagulation and ranibizumab injections (study group), while the other received panretinal photocoagulation alone (control group). All eyes were treated with panretinal photocoagulation in three sessions according to the Early Treatment Diabetic Retinopathy Study guidelines. Contrast sensitivity measurements were performed under photopic conditions (85 cd/m2) with the Visual Contrast Test Sensitivity 6500 chart, allowing for the evaluation of five spatial frequencies with sine wave grating charts: 1.5, 3.0, 6.0, 12.0, and 18.0 cycles per degree (cpd). Outcomes were measured in contrast sensitivity threshold scores among and within groups, from baseline to 1, 3, and 6 months. RESULTS:Fifty-eight eyes (28 in the study group and 30 in the control group) reached the study endpoint. A comparative analysis of changes in contrast sensitivity between the groups showed significant differences mainly in low frequencies as follows: at month 1 in 1.5 cpd (p=0.001) and 3.0 cpd (p=0.04); at month 3 in 1.5 cpd (p=0.016), and at month 6 in 1.5 cpd (p=0.001) and 3.0 cpd (p=0.026) in favor of the study group. CONCLUSIONS:In eyes of patients with non-high-risk proliferative diabetic retinopathy, panretinal photocoagulation treatment with ranibizumab appears to cause less damage to contrast sensitivity compared with panretinal photocoagulation treatment alone. Thus, our evaluation of contrast sensitivity may support the use of ranabizumab as an adjuvant to panretinal photocoagulation for the treatment of proliferative diabetic retinopathy.