Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F₁ scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup.

Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.

Introduction: Ergonomics in ophthalmology is neither taught during professional training nor does it readily receive consideration or priority in clinical practice. Owing to a high prevalence of musculoskeletal disorders among ophthalmologists, ergonomic awareness, and practices are vital in averting this modern epidemic. Objective: To assess the ergonomic practices and prevalence of self-reported musculoskeletal disorders among ophthalmologists in India. Method: An online survey was conducted among ophthalmologists who were registered with the All India Ophthalmological Society of India using a pre-tested and validated questionnaire. Results: The prevalence of self-reported musculoskeletal disorder in the 377 participants was 75.3% (284; 95% confidence interval: 70.6–79.5). Low back pain (149, 52.9%) was the most commonly reported symptom. Musculoskeletal disorder–attributed productivity loss was reported by 58.45% (166) of the respondents. Musculoskeletal disorder was most frequently associated with less than 10 years of work experience (odds ratio: 1.2; 95% confidence interval: 1.1–1.3, p = 0.01) and lack of good clinic (odds ratio: 1.7; 95% confidence interval: 1.1–2.7, p = 0.03) and operation room ergonomic practices (odds ratio: 1.8; 95% confidence interval: 1.1–2.9, p = 0.03). Only 27.9% (105) of the respondents had accessed information related to ergonomics in ophthalmic practice. Conclusion: The self-reported musculoskeletal disorder among ophthalmologists in India is quite high and significant enough to cause some degree of productivity loss. It is thus imperative to emphasize the implementation of good ergonomic practices, especially among relatively young ophthalmologists.

Comparison of dry eyes tests among Smokers and Non-smokers: Does smoking predispose to dry eye?-IJCEO-Print ISSN No:-2395-1443 Online ISSN No:-2395-1451Article DOI No:-10.18231/j.ijceo.2019.132,Indian Journal of Clinical and Experimental Ophthalmology-IP Innovative Publication Pvt Limited, Medical Journals Publicatio

The coronavirus disease (COVID)-19 pandemic has ever since its outbreak been perplexing healthcare generally at all levels. There is a need to identify, analyze and address the bioethical dilemmas that have emerged during the pandemic. This paper presents a narrative review of the published literature on the ethical issues, frameworks, and guidelines in COVID-19-related healthcare and research. An electronic search was conducted on PubMed, Google Scholar, and Science Direct using the search terms “COVID- 19” [AND] ethical issues, clinical trials, resource allocation, ethical guidelines, vaccine allocation. Articles between 2019 and 2020 focusing on ethics were included and analyzed. Fifteen full-text articles in English, one workshop summary, and 5 guidelines were identified and are discussed under the following themes: global response to the pandemic, allocation of resources, conduct of clinical trials, and fair distribution of vaccines and individual patient care. Despite the global and collaborative response to guide the healthcare sector throughout the pandemic, there have been some worrying repercussions in the form of increased vulnerabilities, precarious imbalances in resources, priority settings, exclusion of individuals or groups, exhaustions of healthcare professionals, impaired individual patient care, slowing down of non-covid research as well as scientific, ethical and logistic challenges in COVID and non-COVID research. These can be ethically justifiable only considering the seriousness and urgency of the pandemic. This paper presents some tenacious challenges that must be addressed if ethical reflection is to be effectively implemented in response to this pandemic.

Background Open Science (OS) aims at accelerating responsible scientific research, promoting transparency in data sharing, and ensuring that data is accessible to all citizens. On the other hand, One Health (OH), an interdisciplinary field addressing outbreaks of zoonotic infectious diseases, relies heavily on data sharing. Considering the current emphasis on OS practices, it is crucial to identify the challenges associated with practicing OS within the multi-sectoral framework of OH. This scoping review explores how OS principles intersect with issues of data ownership, privacy, and participation within India’s multi-sectoral OH research ecosystem. Objective This study aims to identify the potential challenges and opportunities at the interfaces of OH, OS, society, and policy. Methods We conducted a scoping review, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched PubMed, Web of Science, Embase (OVID), MEDLINE (OVID), and Global Health to identify the articles. We included original articles and policy briefs on OS, Citizen Science, and OH published between 2013 and 2023. The protocol was preregistered in the Open Science Framework (OSF). Results A total of 46 studies met the inclusion criteria. We reviewed 33 original articles, 4 perspectives, 2 commentaries, 2 case studies, 2 policy briefs, 2 protocols, and 1 case report. Key challenges include barriers to cross-sector collaboration, data sharing, weak inter-ministerial collaboration, and challenges in Citizen Science. The study also highlights significant opportunities for advancing OH through improved data sharing techniques, and enhanced collaborative efforts. Conclusion Addressing these challenges and opportunities may foster effective collaboration, ethical data sharing in OH. These strategies are crucial for advancing OH framework and improving health at the human-animal-environment interface in India and other low-and middle-income countries. This review underscores the importance of integrating OS for the sustainable development of OH initiatives in these settings.