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Purpose: To validate the use of retinal video recording for diabetic retinopathy screening by comparing with standard retinal photography and slit-lamp examination. Design: Evaluation of a new diagnostic technique. Participants: One hundred patients. Methods: All fundus images were captured using standard retinal still photography (FF 450 plus; Carl Zeiss) and retinal video (EyeScan; Ophthalmic Imaging System, Sacramento, CA), followed by a gold standard slit-lamp biomicroscopy examination. All videos and still images were de-identified, randomized, and interpreted by 2 senior consultant ophthalmologists (M.L.T-K. and L.L.). Kappa statistics, sensitivity, and specificity for all the diabetic retinopathy signs and grades were calculated with reference to slit-lamp examination results as the gold standard. Main Outcome Measures: Sensitivity and specificity of video recording for detecting diabetic retinopathy signs and grades. Results: The mean age (±standard deviation [SD]) of participants was 52.8±15.1 years, mean duration of diabetes (±SD) was 13.7±9.7 years, and the mean glycosylated hemoglobin level was 8.0±1.7%. Compared with the gold standard slit-lamp examination results, the sensitivity and specificity of video recording for detecting the presence of any diabetic retinopathy was 93.8% and 99.2%, respectively (ophthalmologist 1), and 93.3% and 95.2%, respectively (ophthalmologist 2). In contrast, the sensitivity and specificity of retinal photography was 91.8% and 98.4%, respectively (ophthalmologist 1), and 92.1% and 96.8%, respectively (ophthalmologist 2), for detection of any diabetic retinopathy. Both imaging methods had 100% sensitivity and specificity in detecting sight-threatening diabetic retinopathy. For overall diabetic retinopathy grading by both ophthalmologists, the measurements of agreement (Cohen''s κ coefficient) between the overall grading obtained from the retinal video versus slit-lamp examination and retinal photography versus slit-lamp examination were more than 0.90. Technical failure rate for retinal video recording and retinal photography were 7.0% and 5.5%, respectively. Conclusions: This study demonstrated that retinal video recording was equally as effective as retinal photography in the subjects evaluated in this study. It is a novel alternative diabetic retinopathy screening technique that not only offers primary eye care providers the opportunity to view numerous retinal fields within a short period but also is easy to learn by nonexperienced personnel with minimal training. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented regarding the removal of ticks from ocular tissues using en bloc removal technique with excision to prevent inflammation and infection.

Background:The introduction of artificial intelligence (AI) in medicine has raised significant ethical, economic, and scientific controversies. Introduction:Because an explicit goal of AI is to perform processes previously reserved for human clinicians and other health care personnel, there is justified concern about the impact on patient safety, efficacy, equity, and liability. Discussion:Systems for computer-assisted and fully automated detection, triage, and diagnosis of diabetic retinopathy (DR) from retinal images show great variation in design, level of autonomy, and intended use. Moreover, the degree to which these systems have been evaluated and validated is heterogeneous. We use the term DR AI system as a general term for any system that interprets retinal images with at least some degree of autonomy from a human grader. We put forth these standardized descriptors to form a means to categorize systems for computer-assisted and fully automated detection, triage, and diagnosis of DR. The components of the categorization system include level of device autonomy, intended use, level of evidence for diagnostic accuracy, and system design. Conclusion:There is currently minimal empirical basis to assert that certain combinations of autonomy, accuracy, or intended use are better or more appropriate than any other. Therefore, at the current stage of development of this document, we have been descriptive rather than prescriptive, and we treat the different categorizations as independent and organized along multiple axes. [ABSTRACT FROM AUTHOR]

Abstract: Visual neglect is a common, yet frequently overlooked, neurological disorder following stroke characterized by a deficit in attention and appreciation of stimuli on the contralesional side of the body. It has a profound functional impact on affected individuals. A assessment and management of this condition are hindered, however, by the lack of professional awareness and clinical guidelines. Recent evidence suggests that the underlying deficit in visual attention is due to a disrupted internalized representation of the outer world rather than a disorder of sensory inputs. Dysfunction of the cortical domains and white-matter tracts, as well as inter-hemispheric imbalance, have been implicated in the various manifestations of visual neglect. Optimal diagnosis requires careful history-taking from the patient, family, and friends, in addition to clinical assessment with the line bisection test, the star cancellation test, and the Catherine Bergego Scale. Early recognition and prompt rehabilitation employing a multidisciplinary approach is desirable. Although no treatment has been definitively shown to be of benefit, those with promise include prism adaptation, visual scanning therapy, and virtual reality–based techniques. Further high quality research to seek optimum short- and long-term rehabilitative strategies for visual neglect is required. [Copyright &y& Elsevier]

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions. [ABSTRACT FROM AUTHOR]