Current opinion in ophthalmology
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To summarize how big data and artificial intelligence technologies have evolved, their current state, and next steps to enable future generations of artificial intelligence for ophthalmology. ⋯ Future requirements for big data and artificial intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of artificial intelligence by promoting standards for data labels, data sharing, artificial intelligence model architecture sharing, and accessible code and APIs.
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Curr Opin Ophthalmol · Sep 2020
Comparative StudyOcular trauma during COVID-19 stay-at-home orders: a comparative cohort study.
The aim of this study was to report characteristics of patients presenting with serious ocular injuries during the COVID-19 stay-at-home orders. ⋯ During the COVID-19 pandemic, patients with ocular trauma were more likely to have injuries sustained at home and have additional barriers to care. These changes underscore a need for targeted interventions to optimize emergent eye care during a pandemic.
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Curr Opin Ophthalmol · Sep 2020
ReviewConsiderations in the use of slit lamp shields to reduce the risk of respiratory virus transmission in coronavirus disease 2019.
The use of slit lamp shields has been recommended by the American Academy of Ophthalmology as an infection control measure during the coronavirus disease 2019 pandemic. However, there is limited evidence regarding its efficacy to reduce viral transmission risks. We aim to provide an evidence-based approach to optimize the use of slit lamp shields during clinical examination. ⋯ Slit lamp shields serve as a barrier for large droplets, but its protection against smaller droplets is undetermined. It should be large, positioned close to the patient, and used in tandem with routine basic disinfection practices.
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Curr Opin Ophthalmol · Sep 2020
ReviewEbola, COVID-19, and emerging infectious disease: lessons learned and future preparedness.
To highlight the lessons learned from the Ebola outbreak that may inform our approach to the COVID-19 pandemic, particularly related to the widespread disruption of healthcare, ophthalmic disease manifestations, and vision health systems strengthening for future outbreaks. ⋯ Thoroughly understanding the ophthalmic findings and transmission risk associated with COVID-19 is paramount during this pandemic, providing additional measures of safety while resuming ophthalmic care for all patients. Vision health systems preparedness measures developed during recent EVD outbreaks and the current pandemic provide models for ophthalmic clinical practice, research, and education, as we continue to address COVID-19 and future emerging infectious disease threats.
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Curr Opin Ophthalmol · Sep 2020
ReviewFundamentals of artificial intelligence for ophthalmologists.
As artificial intelligence continues to develop new applications in ophthalmic image recognition, we provide here an introduction for ophthalmologists and a primer on the mechanisms of deep learning systems. ⋯ Deep learning systems have begun to demonstrate a reliable level of diagnostic accuracy equal or better to human graders for narrow image recognition tasks. However, challenges regarding the use of deep learning systems in ophthalmology remain. These include trust of unsupervised learning systems and the limited ability to recognize broad ranges of disorders.